Visualisation de l’information

Vous trouverez sur cette page différentes ressources en ligne et une bibliographie portant sur la visualisation de l’information. (Cette page est en cours de développement)

a	b	c	d	e
f	g	h	i	j

Sources et références
a: Timeline of 20th c. Art and New Media par Rama Hoetzlein (c)2009
b: What Makes Good Information Design par David McCandless V1.0//Nov.2009
c: Tokyo | Cairo: Comparing Obama’s Foreign Policy Speeches par Jer Thorp 16 novembre 2009.
d: Application Webpages as Graphs sur le site d’Amazon.com
e: Projet “NYT: this was 2009” par Jer Thorp
f: The Proto-Indo-European Language Tree par Intersol (c) 1996-2008
g: Well-Formed, Citation Pattern en collaboration avec Moritz Stefaner et Eigenfactor Project.
h: “Distorsion Fisheye X” avec Flare
i: DocuBurst: Visualizing Document Content using Language Structure par Christopher Collins, Sheelagh Carpendale et Gerald Penn
j: Graphical visualization of text similarities par Magnus Rembold et Jurgen Spath, 2006

Personnalités

Edward Tufte Professeur en statistique et design de l’information au Département de Science Politique de l’Université Yale, son livre The Visual Display of Quantitative Information est un ouvrage de référence en matière de design de l’information. À écouter, une entrevue de 10 minutes réalisée par VizWorld.
Manuel Lima Chercheur, designer et créateur du site Visual Complexity, il a été nommé parmi les 50 personnalités les plus créatives en 2009 par le magazine londonien Creativity.
Nathan Yau est un étudiat an doctorat au Département de Statistique de UCLA dont l’intérêt de recherche est la visualisation des informations personnelles. Son Blog FlowingData traite d’ailleurs de ce sujet et il est également le créateur de l’application Twitter your.flowingdata .
Matthew Ericson Vice-Directeur du graphisme au New York Times.
Jer Thorp Artiste de Vancouver qui est aussi instructeur en “Electronic Media Design” au Langara College.
Cool Infographics a publié une liste de 37 personnes liées à l’infographie à suivre sur Twitter.
datavisualization.ch a publié une liste complémentaire de 30 personnes liées à l’infographie à suivre sur Twitter.
Digup.tv offre plusieurs entrevues vidéo avec les personnalités actuelles en visualisation de l’information.

Blogs

Information Aesthetics
Information is Beautiful par David McCandless, auteur du livre The Visual Miscellaneum: A Colorful Guide To The World’s Most Consequential Trivia, designer autodidacte et journaliste indépendant spécialisé dans le domaine du design de l’information.
VC Blog est un dérivé du site Visual Complexity.
The Anatomy Of An Infographic: 5 Steps To Create A Powerful Visual est un article publié par Sneh Roy sur le site SpyreStudio.com. qui donne un bon apercu des principales étapes menant à la création de visuel efficace.
Innovative Interactivity a publié Data visualization tools for free or cheap (une liste de logiciels de visualisation abordables ou gratuits).
7 Things You Should Know About Data Visualization II est un article publié par Educause.

Magazines

Visualisation Magazine
Good.is et sa section nommée Transparency.

Web

Visual Complexity est une vitrine exposant des projets de recherche provenant de disciplines variées qui ont en commun l’utilisation de diverses méthodes de visualisation. Le site est une initiative de Manuel Lima.
Smashing Magazine a publié en septembre 2009 une liste importante des ressources actuelles portant sur la visualisation de l’information et l’infographie.
Projet ReMap (à partir du site Visual Complexity) utilisant le moteur de recherche sémantique Bestiario.

Applications et exemples

Google Swirl est une application de Google Labs qui présente une interface d’exploration des résultats de recherche d’images sous forme de grappe.
Visual Understanding Environment (VUE) est un projet de la Tufts University. Ce logiciel libre est un environnement visuel servant à la gestion de ressources numériques.
Synesketch est une application Web libre basée sur la reconnaissance et la visualisation des émotions textuelles.
ASK KEN Visual Knowledge Browser.
Webpages as Graphs permet de visualiser a strucuture des sites Web (voir exemple du site de l’Université de Montréal)
Flare
Pearltrees est un outil permettant de garder et gérer des hyperliens Web sous forme de réseaux de “perles”.

Bibliographie thématique

[2009,book] bibtex

T. Segaran, J. Hammerbacher, S. Toby, and H. Jeff, Beautiful Data: The Stories Behind Elegant Data Solutions, 1 ed., O’Reilly Media, 2009.

@book{segaran_beautiful_2009, edition = {1}, title = {Beautiful Data: The Stories Behind Elegant Data Solutions}, isbn = {0596157118}, shorttitle = {Beautiful Data}, publisher = {{O'Reilly} Media}, author = {Toby Segaran and Jeff Hammerbacher and Segaran Toby and Hammerbacher Jeff}, month = aug, year = {2009}, keywords = {Visualisation de l'information} },
[2009,book] bibtex

A. Vit and B. G. Palacio, Graphic Design, Referenced: A Visual Guide to the Language, Applications, and History of Graphic Design, Rockport Publishers, 2009.

@book{vit_graphic_2009, title = {Graphic Design, Referenced: A Visual Guide to the Language, Applications, and History of Graphic Design}, isbn = {1592534473}, shorttitle = {Graphic Design, Referenced}, publisher = {Rockport Publishers}, author = {Armin Vit and Bryony Gomez Palacio}, month = jul, year = {2009}, keywords = {Design, Graphique, Visualisation de l'information} },
[2008,book] bibtex

R. Klanten, N. Bourquin, and T. Tissot, Data Flow: Visualising Information in Graphic Design, Dgv, 2008.

@book{klanten_data_2008, title = {Data Flow: Visualising Information in Graphic Design}, isbn = {3899552172}, shorttitle = {Data Flow}, publisher = {Dgv}, author = {Robert Klanten and Nicolas Bourquin and Thibaud Tissot}, month = sep, year = {2008}, keywords = {Visualisation de l'information} },
[2008,article] bibtex

G. G. Yen and Z. Wu, "Ranked centroid projection : a data visualization approach with self-organizing maps," IEEE Transactions on Neural Networks, vol. 19, iss. 2, pp. 245-259, 2008.

@article{yen_ranked_2008, title = {Ranked centroid projection : a data visualization approach with self-organizing maps}, volume = {19}, issn = {10459227}, shorttitle = {Ranked Centroid Projection}, doi = {10.1109/TNN.2007.905858}, abstract = {The self-organizing map {(SOM)} is an efficient tool for visualizing high-dimensional data. In this paper, the clustering and visualization capabilities of the {SOM,} especially in the analysis of textual data, i.e., document collections, are reviewed and further developed. A novel clustering and visualization approach based on the {SOM} is proposed for the task of text mining. The proposed approach first transforms the document space into a multidimensional vector space by means of document encoding. Afterwards, a growing hierarchical {SOM} {(GHSOM)} is trained and used as a baseline structure to automatically produce maps with various levels of detail. Following the {GHSOM} training, the new projection method, namely the ranked centroid projection {(RCP),} is applied to project the input vectors to a hierarchy of {2-D} output maps. The {RCP} is used as a data analysis tool as well as a direct interface to the data. In a set of simulations, the proposed approach is applied to an illustrative data set and two real-world scientific document collections to demonstrate its applicability. {ABSTRACT} {FROM} {AUTHOR} Copyright of {IEEE} Transactions on Neural Networks is the property of {IEEE} and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. {(Copyright} applies to all Abstracts)}, number = {2}, journal = {{IEEE} Transactions on Neural Networks}, author = {Gary G. Yen and Zheng Wu}, month = feb, year = {2008}, keywords = {Fouille de donnée, Fouille de texte, Réseau de neurones, Visualisation de l'information}, pages = {245--259}, annote = {{{\textless}p{\textgreater}Accession} Number: 31171851; Yen, Gary G. 1; Email Address: gyen@okstate.edu; Zheng Wu 1; Affiliations: 1: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, {OK} 74078 {USA;} Issue Info: Feb2008, Vol. 19 Issue 2, p245; Thesaurus Term: {NEURAL} networks {(Computer} science); Thesaurus Term: {VISUAL} programming languages {(Computer} science); Thesaurus Term: {DATA} mining; Subject Term: {SELF-organizing} maps; Subject Term: {TEXT} mining {(Information} retrieval); Subject Term: {CONTENT} mining; Subject Term: {SELF-organizing} systems; Subject Term: {VECTOR} analysis; Subject Term: {ENCODING;} {Author-Supplied} Keyword: Data visualization; {Author-Supplied} Keyword: document clustering; {Author-Supplied} Keyword: self-organizing map {(SOM);} Number of Pages: 15p; Illustrations: 3 charts, 6 diagrams, 15 graphs, 2 bw; Document Type: Article{\textless}/p{\textgreater}} },
[2008,article] bibtex

L. Wanner, R. Baeza-Yates, S. Brugmann, J. Codina, B. Diallo, E. Escorsa, M. Giereth, Y. Kompatsiaris, S. Papadopoulos, E. Pianta, G. Piella, I. Puhlmann, G. Rao, M. Rotard, P. Schoester, L. Serafini, and V. Zervaki, "Towards content-oriented patent document processing," World Patent Information, vol. 30, iss. 1, pp. 21-33, 2008.

@article{wanner_towards_2008, title = {Towards content-oriented patent document processing}, volume = {30}, url = {http://www.sciencedirect.com/science/article/B6V5D-4NT93N4-1/2/28ca16a1b7ef2db1ffd30377e0b37df4}, abstract = {In this article, we present ongoing work on an advanced patent processing service {PATExpert.} The central assumption underlying {PATExpert} is that in order to meet the needs of the users of patent processing services, recourse must be made to the content of patent material. We introduce a content representation schema for patent documentation and sketch the design of techniques that facilitate the integration of this schema into the patent processing cycle. Two types of techniques are discussed. Techniques of the first type facilitate the access to the content of patent documentation provided in a textual format - be it by the human reader or by the machine - in that they rephrase and summarize the documentation and map it onto a formal semantic representation. Techniques of the second type operate on the content representation. At this stage, {PATExpert} is explored in two technology areas - optical recording devices and machine tools. The work is being carried out in the framework of an {R\&D-project} partially funded by the European Commission.}, number = {1}, journal = {World Patent Information}, author = {Leo Wanner and Ricardo {Baeza-Yates} and Soren Brugmann and Joan Codina and Barrou Diallo and Enric Escorsa and Mark Giereth and Yiannis Kompatsiaris and Symeon Papadopoulos and Emanuele Pianta and Gemma Piella and Ingo Puhlmann and Gautam Rao and Martin Rotard and Pia Schoester and Luciano Serafini and Vasiliki Zervaki}, year = {2008}, keywords = {Classification, Extraction d'information, Ontologie, Visualisation de l'information}, pages = {21--33} },
[2008,inproceedings] bibtex

H. Kim, J. Hwang, B. Suh, Y. Nah, and Hyung-Soo, "Semi-automatic ontology construction for visual media web service," , Suwon, Korea, 2008, pp. 69-73.

@inproceedings{kim_semi-automatic_2008, address = {Suwon, Korea}, title = {Semi-automatic ontology construction for visual media web service}, isbn = {978-1-59593-993-7}, url = {http://portal.acm.org/ft_gateway.cfm?id=1352808&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435}, doi = {10.1145/1352793.1352808}, abstract = {Ontology is a description of the concepts and relationships that can exist for an agent or a community of agents. Ontologies are a key enabling technology for the Semantic Web. Previously, ontologies have been built manually by knowledge engineers using ontology editors. In this paper, we propose a semiautomatic ontology construction scheme for visual media data brokers and providers. We apply data mining techniques on sets of image data groups to determine general term relationships and utilize {WordNet} to determine semantic relationships between terms. Even though our research has been started for visual media data retrieval, the proposed scheme can be also applied to other problem domains.}, publisher = {{ACM}}, author = {Hayoung Kim and Jaeil Hwang and Bowon Suh and Yunmook Nah and {Hyung-Soo} Mok}, year = {2008}, keywords = {Ontologie, Recherche d'information, Visualisation de l'information}, pages = {69--73}, annote = {{{\textless}p{\textgreater}kimHayoung2008.pdf{\textless}/p{\textgreater}}} },
[2007,book] bibtex

F. Moretti, Graphs Maps Trees, Verso Press USA, 2007.

@book{moretti_graphs_2007, title = {Graphs Maps Trees}, isbn = {1844671852}, publisher = {Verso Press {USA}}, author = {Franco Moretti}, month = aug, year = {2007}, keywords = {Visualisation de l'information} },
[2007,inproceedings] bibtex

A. Don, E. Zheleva, M. Gregory, S. Tarkan, L. Auvil, T. Clement, B. Shneiderman, and C. Plaisant, "Discovering interesting usage patterns in text collections: integrating text mining with visualization," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, Lisbon, Portugal, 2007, pp. 213-222.

@inproceedings{don_discovering_2007, address = {Lisbon, Portugal}, title = {Discovering interesting usage patterns in text collections: integrating text mining with visualization}, isbn = {978-1-59593-803-9}, shorttitle = {Discovering interesting usage patterns in text collections}, url = {http://portal.acm.org/citation.cfm?id=1321473}, doi = {10.1145/1321440.1321473}, abstract = {This paper addresses the problem of making text mining results more comprehensible to humanities scholars, journalists, intelligence analysts, and other researchers, in order to support the analysis of text collections. Our system, {FeatureLens1,} visualizes a text collection at several levels of granularity and enables users to explore interesting text patterns. The current implementation focuses on frequent itemsets of n-grams, as they capture the repetition of exact or similar expressions in the collection. Users can find meaningful co-occurrences of text patterns by visualizing them within and across documents in the collection. This also permits users to identify the temporal evolution of usage such as increasing, decreasing or sudden appearance of text patterns. The interface could be used to explore other text features as well. Initial studies suggest that {FeatureLens} helped a literary scholar and 8 users generate new hypotheses and interesting insights using 2 text collections.}, booktitle = {Proceedings of the sixteenth {ACM} conference on Conference on information and knowledge management}, publisher = {{ACM}}, author = {Anthony Don and Elena Zheleva and Machon Gregory and Sureyya Tarkan and Loretta Auvil and Tanya Clement and Ben Shneiderman and Catherine Plaisant}, year = {2007}, keywords = {Fouille de texte, Visualisation de l'information}, pages = {213--222}, annote = {{{\textless}p{\textgreater}anthonyDon2007.pdf{\textless}/p{\textgreater}}} },
[2007,article] bibtex

J. Gelernter, "Visual Classification with Information Visualization (Infoviz) for Digital Library Collections," Knowl. Org, vol. 34, iss. 3, 2007.

@article{gelernter_visual_2007, title = {Visual Classification with Information Visualization {(Infoviz)} for Digital Library Collections}, volume = {34}, url = {http://www.cs.cmu.edu/afs/cs/Web/People/gelernter/classification.pdf}, number = {3}, journal = {Knowl. Org}, author = {Judith Gelernter}, year = {2007}, keywords = {Bibliothèque numérique, Visualisation de l'information} },
[2007,misc] bibtex

Cyberinfrastructure Vision for the "1st Century DiscoveryNational Science Foundation, 2007.

@misc{_cyberinfrastructure_2007, title = {Cyberinfrastructure Vision for the "1st Century Discovery}, url = {http://www.clal.cornell.edu/vcla/sites/all/themes/vclatheme/images/CI_Vision_March07.pdf}, publisher = {National Science Foundation}, month = mar, year = {2007}, keywords = {Visualisation de l'information}, annote = {{\textless}p{\textgreater}ndf2007.pdf{\textless}/p{\textgreater}} },
[2007,misc] bibtex

R. Lengler and M. J. Eppler, Towards A Periodic Table of Visualization Methods for Management, 2007.

@misc{lengler_towardsperiodic_2007, title = {Towards A Periodic Table of Visualization Methods for Management}, url = {http://www.visual-literacy.org/periodic_table/periodic_table.pdf}, author = {Ralph Lengler and Martin J. Eppler}, year = {2007}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}ralphLengler2007.pdf{\textless}/p{\textgreater}}} },
[2006,article] bibtex

"Balancing Systematic and Flexible Exploration of Social Networks," IEEE Transactions on Visualization and Computer Graphics, vol. 12, iss. 5, pp. 693-700, 2006.

@article{_balancing_2006, title = {Balancing Systematic and Flexible Exploration of Social Networks}, volume = {12}, url = {http://portal.acm.org/citation.cfm?id=1187850}, abstract = {Social network analysis {(SNA)} has emerged as a powerful method for understanding the importance of relationships in {networks.However,} interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly {manner.This} results in exploration that is largely {opportunistic.Our} contributions are techniques to help structural analysts understand social networks more {effectively.We} present {SocialAction,} a system that uses attribute ranking and coordinated views to help users systematically examine numerous {SNA} {measures.Users} can (1) flexibly iterate through visualizations of measures to gain an overview, filter nodes, and find outliers, (2) aggregate networks using link structure, find cohesive subgroups, and focus on communities of interest, and (3) untangle networks by viewing different link types separately, or find patterns across different link types using a matrix {overview.For} each operation, a stable node layout is maintained in the network visualization so users can make {comparisons.SocialAction} offers analysts a strategy beyond opportunism, as it provides systematic, yet flexible, techniques for exploring social networks.}, number = {5}, journal = {{IEEE} Transactions on Visualization and Computer Graphics}, year = {2006}, keywords = {Visualisation de l'information}, pages = {693--700}, annote = {{{\textless}p{\textgreater}admaPerer2006.pdf{\textless}/p{\textgreater}}} },
[2006,article] bibtex

I. Samoylenko, T.-C., W.-C., and C.-M., "Visualizing the scientific world and its evolution," Journal of the american society for information science and technology, vol. 57, iss. 11, pp. 1461-1469, 2006.

@article{samoylenko_visualizingscientific_2006, title = {Visualizing the scientific world and its evolution}, volume = {57}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.9195}, doi = {10.1.1.102.9195}, abstract = {We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees {(MSTs)} and a two-dimensional map of scientific journals using the database of the Science Citation Index {(SCI)} during 1994–2001. The structures of constructed {MSTs} are consistent with the sorting of {SCI} categories. The map of science is constructed based on our {MST} results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.}, number = {11}, journal = {Journal of the american society for information science and technology}, author = {I. Samoylenko and {T.-C.} Chao and {W.-C.} Liu and {C.-M.} Chen}, year = {2006}, keywords = {Visualisation de l'information}, pages = {1461--1469}, annote = {{{\textless}p{\textgreater}samoylenkoL2006.pdf{\textless}/p{\textgreater}}} },
[2006,article] bibtex

Ibekwe-SanJuan, "Constructing and maintaining knowledge organization tools : a symbolic approach," Journal of Documentation, vol. 62, iss. 2, pp. 229-50, 2006.

@article{fidelia_constructing_2006, title = {Constructing and maintaining knowledge organization tools : a symbolic approach}, volume = {62}, url = {http://dx.doi.org/10.1108/00220410610653316}, abstract = {Purpose - To propose a comprehensive and semi-automatic method for constructing or updating knowledge organization tools such as thesauri. Design/methodology/approach - The paper proposes a comprehensive methodology for thesaurus construction and maintenance combining shallow {NLP} with a clustering algorithm and an information visualization interface. The resulting system {TermWatch,} extracts terms from a text collection, mines semantic relations between them using complementary linguistic approaches and clusters terms using these semantic relations. The clusters are mapped onto a {2D} using an integrated visualization tool. Findings - The clusters formed exhibit the different relations necessary to populate a thesaurus or ontology: synonymy, generic/specific and relatedness. The clusters represent, for a given term, its closest neighbours in terms of semantic relations. Practical implications - This could change the way in which information professionals (librarians and documentalists) undertake knowledge organization tasks. {TermWatch} can be useful either as a starting point for grasping the conceptual organization of knowledge in a huge text collection without having to read the texts, then actually serving as a suggestive tool for populating different hierarchies of a thesaurus or an ontology because its clusters are based on semantic relations. Originality/value - This lies in several points: combined use of linguistic relations with an adapted clustering algorithm, which is scalable and can handle sparse data. The paper proposes a comprehensive approach to semantic relations acquisition whereas existing studies often use one or two approaches. The domain knowledge maps produced by the system represents an added advantage over existing approaches to automatic thesaurus construction in that clusters are formed using semantic relations between domain terms. Thus while offering a meaningful synthesis of the information contained in the original corpus through clustering, the results can be used for knowledge organization tasks (thesaurus building and ontology population) The system also constitutes a platform for performing several knowledge-oriented tasks like science and technology watch, textmining, query refinement}, number = {2}, journal = {Journal of Documentation}, author = {{Ibekwe-SanJuan} Fidelia}, year = {2006}, keywords = {Cluster, Gestion des connaissances, Langage naturel, Ontologie, Thésaurus, Visualisation de l'information}, pages = {229--50}, annote = {{{\textless}p{\textgreater}Copyright} 2006, The Institution of Engineering and Technology 8898529 0022-0418 knowledge organization tools thesauri thesaurus construction natural language processing clustering algorithm information visualization interface complementary linguistic integrated visualization tool information professionals ontology population textmining query refinement{\textless}/p{\textgreater}} },
[2006,article] bibtex

Z. Shen, Kwan-Liu, and T. Eliassi-Rad, "Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction," IEEE Transactions on Visualization and Computer Graphics, vol. 12, iss. 6, pp. 1427-1439, 2006.

@article{shen_visual_2006, title = {Visual Analysis of Large Heterogeneous Social Networks by Semantic and Structural Abstraction}, volume = {12}, abstract = {Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, {OntoVis,} for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). {OntoVis} is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, {OntoVis} also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of {OntoVis} are illustrated with several case studies.}, number = {6}, journal = {{IEEE} Transactions on Visualization and Computer Graphics}, author = {Zeqian Shen and {Kwan-Liu} Ma and Tina {Eliassi-Rad}}, year = {2006}, keywords = {Graphique, Visualisation de l'information}, pages = {1427--1439}, annote = {{{\textless}p{\textgreater}shenZeqian2006.pdf{\textless}/p{\textgreater}}} },
[2006,book] bibtex

C. Chen, Information visualization : beyond the horizon, New York: Springer, 2006.

@book{chen_information_2006, address = {New York}, title = {Information visualization : beyond the horizon}, isbn = {9781846283406}, publisher = {Springer}, author = {Chaomei Chen}, year = {2006}, keywords = {Visualisation de l'information} },
[2006,misc] bibtex

J. Vendetti, Ontology visualizationStandford university, Palo Alto, California.: , 2006.

@misc{vendetti_ontology_2006, address = {Standford university, Palo Alto, California.}, type = {{PDF} Tutorial}, title = {Ontology visualization}, abstract = {This tutorial will focus on several Protégé plug-ins that have been developed specifically for the purpose of visualizing ontologies. The first portion of the tutorial will be a hands-on walk-through of the graph widget tutorial that is available on the Protégé Web site. The graph widget is a custom slot widget plug-in that allows users to graphically create and populate instances of classes. It is very useful as a) an alternative to Protégé Forms for entering instance data, b) as a way to visualize networks of instances, and c) as a way to visualize relationships between instances. The second portion of the tutorial will be a demonstration of the Ontoviz and {TGViz} tab plug-ins. If time permits at the end of the tutorial, there will also be a demonstration of a new visualization plug-in called the Impact \& Alignment Tab, which is a work in progress and will be released in the fall. The target audience for this tutorial are Protégé users with little exposure to the existing visualization plug-ins that are currently available. If users plan to follow the hands-on portion of the tutorial, basic experience with navigating the Protégé user interface will be assumed. Attendees should know how to perform rudimentary tasks such as opening projects and creating new classes, slots, and instances.}, author = {Jennifer Vendetti}, month = jul, year = {2006}, keywords = {Ontologie, Visualisation de l'information}, annote = {{{\textless}p{\textgreater}vendettiJennifer2006.pdf{\textless}/p{\textgreater}}} },
[2006,misc] bibtex

CIGREF, Guide de recensement des outils de collecte, de traitement et de visualisation de l’information, 2006.

@misc{cigref_guide_2006, title = {Guide de recensement des outils de collecte, de traitement et de visualisation de l'information}, url = {http://crrm.u-3mrs.fr/blog/IMG/pdf/Recensement_des_outils_IE.pdf}, abstract = {Les outils de collecte, de traitement et de visualisation de {l'Information} constituent un support essentiel de toute démarche d'lntelligence Economique {d'Entreprise.} L'industrie logicielle française est composée de nombreux acteurs industriels et laboratoires dont les technologies, performantes et souvent innovantes, demeurent, pour le moment, mal connues. Afin d'identifier ces outils et ainsi donner aux {DS} et autres directions métiers une meilleure visibilité de l'état du marché, le Cercle {d'Intelligence} Economique du {CIGREF} a souhaité diffuser ce document de synthèse, fruit d'une étroite collaboration avec la mission du Haut responsable a {l'Intelligence} Economique et la {DCSSI} au Secretariat Général de la Défense Nationale.}, author = {{CIGREF}}, year = {2006}, keywords = {Visualisation de l'information}, annote = {{\textless}p{\textgreater}cigref2006.pdf{\textless}/p{\textgreater}} },
[2006,inproceedings] bibtex

J. Strychowski, "Concept glossary manager : topic maps engine and navigator," in Charting the topic maps research and applications landscape : first international workshop on topic map research and applications, TMRA 2005, Leipzig, Germany, october 6-7, 2005 : revised selected papers, Berlin; Heidelberg, 2006, pp. 26-41.

@inproceedings{strychowski_concept_2006, address = {Berlin; Heidelberg}, series = {Lecture notes in computer science; 3873}, title = {Concept glossary manager : topic maps engine and navigator}, isbn = {978-3-540-32527-7}, abstract = {The Office Objects Concept Glossary Manager {(CGM),} which has been designed by the author as a software component of the {ICONS} system, helps to create, edit and visualize topic maps. Interesting features of the {CGM} are distributed topic maps processing, user rights management, topic states and versions management, ontology driven generative user interfaces and Topic Maps Script Language {(TMSL).} This paper also overviews an example application of the component. In the final section some weaknesses of the {CGM} are identified and possible improvements are suggested.}, booktitle = {Charting the topic maps research and applications landscape : first international workshop on topic map research and applications, {TMRA} 2005, Leipzig, Germany, october 6-7, 2005 : revised selected papers}, publisher = {Springer}, author = {Jakub Strychowski}, year = {2006}, keywords = {Visualisation de l'information}, pages = {26--41}, annote = {{{\textless}p{\textgreater}strychowskiJakun2006.pdf{\textless}/p{\textgreater}}} },
[2006,incollection] bibtex

K. X. S. de Souza, J. Davis, and S. R. M. de Evangelista, "Aligning ontologies, evaluating concept similarities and visualizing results." Berlin; Heidelberg: Springer, 2006, pp. 211-236.

@incollection{sampaio_de_souza_aligning_2006, address = {Berlin; Heidelberg}, series = {Lecture notes in computer science; 3870}, title = {Aligning ontologies, evaluating concept similarities and visualizing results}, abstract = {Ontologies have been created for many different subjects and by independent groups around the world. The nonexistence of a commonly accepted and used general purpose upper-ontology makes it difficult to integrate these ontologies through merge and alignment operations. The majority of the algorithms proposed so far rely on syntactic analysis, disregarding the structural properties of the source ontologies. In our previous work, we proposed an alignment method that considers the structural properties of an upper-ontology constructed using a thesaurus and Formal Concept Analysis technique {(FCA).} We also analyzed the {FCA’s} lattice structure and proposed a measure of similarity based on Tversky’s model, which allowed us to identify closely related concepts in different source ontologies. In this paper, we apply the alignment method to ontologies developed for a completely different domain, and enhance the solution by providing a navigational aid for the lattice. It is well known that one of the main drawbacks of the application of {FCA} is that the resulting lattice soon becomes cluttered when the number of objects and attributes increases. The proposed solution is based on hyperbolic visualization and on structural elements of the lattice. Keywords: ontology alignment, Formal Concept Analysis, lattice visualization, similarity measures}, booktitle = {Journal on data semantics V}, publisher = {Springer}, author = {Kleber Xavier Sampaio de Souza and Joseph Davis and Silvio Roberto de Medeiros Evangelista}, year = {2006}, keywords = {Ontologie, Visualisation de l'information}, pages = {211--236}, annote = {{{\textless}p{\textgreater}sampaioKleber2006.pdf{\textless}/p{\textgreater}}} },
[2006,inproceedings] bibtex

C. Plaisant, J. Rose, B. Yu, L. Auvil, M. G. Kirschenbaum, M. N. Smith, T. Clement, and G. Lord, "Exploring erotics in Emily Dickinson’s correspondence with text mining and visual interfaces," , Chapel Hill, NC, USA, 2006, pp. 141-150.

@inproceedings{plaisant_exploring_2006, address = {Chapel Hill, {NC,} {USA}}, title = {Exploring erotics in Emily Dickinson's correspondence with text mining and visual interfaces}, isbn = {1-59593-354-9}, url = {http://portal.acm.org/ft_gateway.cfm?id=1141781&type=pdf&coll=ACM&dl=ACM&CFID=76094285&CFTOKEN=90415435}, doi = {10.1145/1141753.1141781}, abstract = {This paper describes a system to support humanities scholars in their interpretation of literary work. It presents a user interface and web architecture that integrates text mining, a graphical user interface and visualization, while attempting to remain easy to use by non specialists. Users can interactively read and rate documents found in a digital libraries collection, prepare training sets, review results of classification algorithms and explore possible indicators and explanations. Initial evaluation steps suggest that there is a rationale for "provocational" text mining in literary interpretation.}, publisher = {{ACM}}, author = {Catherine Plaisant and James Rose and Bei Yu and Loretta Auvil and Matthew G. Kirschenbaum and Martha Nell Smith and Tanya Clement and Greg Lord}, year = {2006}, keywords = {Fouille de texte, Visualisation de l'information}, pages = {141--150}, annote = {{{\textless}p{\textgreater}plaisantCatherine2006.pdf{\textless}/p{\textgreater}}} },
[2006,article] bibtex

C. V. Fabritius, "Finding the best visualization of an ontology," Journal of the Operational Research Society, vol. 57, iss. 12, pp. 1482-1490, 2006.

@article{fabritius_findingbest_2006, title = {Finding the best visualization of an ontology}, volume = {57}, url = {http://www.ingentaconnect.com/content/pal/01605682/2006/00000057/00000012/art00010}, abstract = {An ontology is a classification model for a given domain. In information retrieval, ontologies are used to perform broad searches. An ontology can be visualized as nodes and edges. Each node represents an element and each edge a relation between a parent and a child element. Working with an ontology becomes easier with a visual representation. An idea is to use the expressive power of {3D} representation to provide visualization for the user. In this paper, we propose a new method for positioning the elements of the visualized concept lattice in the {3D} world based on operations research {(OR)} methods. One method uses a discrete location model to create an initial solution and we propose heuristic methods to further improve the visual result. We evaluate the visual results according to our success criteria and the feedback from users. Running times of the heuristic indicate that an improved version should be feasible for on-line processing and what-if analysis of ontologies.}, number = {12}, journal = {Journal of the Operational Research Society}, author = {C. V. Fabritius}, year = {2006}, keywords = {Classification, Ontologie, Visualisation de l'information}, pages = {1482--1490} },
[2005,article] bibtex

J. A. Pretorius, "Visual analysis for ontology engineering," Journal of Visual Languages \& Computing, vol. 16, iss. 4, pp. 359-381, 2005.

@article{pretorius_visual_2005, title = {Visual analysis for ontology engineering}, volume = {16}, url = {http://www.sciencedirect.com/science/article/B6WMM-4FH0D97-2/2/0801066a8aabfdb4edb217c2b762b333}, abstract = {An ontology may be decomposed into a layer of binary fact types and a layer of application specific constraints imposed on these fact types. An ontology base is a large set of binary fact types called lexons. This paper presents {LexoVis,} a lexon visualization tool that addresses the inherent size and scale of ontology bases. {LexoVis} facilitates the analysis of lexons by providing an ordered visual representation. This representation offers overview and detail by employing the graphical fisheye view. Different ordering and clustering heuristics incorporated in {LexoVis} lead to insights not explicit in text-based representations of lexons.}, number = {4}, journal = {Journal of Visual Languages \& Computing}, author = {A. Johannes Pretorius}, year = {2005}, keywords = {Ontologie, Visualisation de l'information}, pages = {359--381} },
[2005,incollection] bibtex

B. Zhu and H. Chen, "Information Visualization." , 2005, vol. 39, pp. 139-177.

@incollection{zhu_information_2005, title = {Information Visualization}, volume = {39}, booktitle = {Information Science and Technology}, author = {Bin Zhu and Hsinchun Chen}, year = {2005}, keywords = {Visualisation de l'information}, pages = {139--177} },
[2005,inproceedings] bibtex

T. D. Breaux and J. W. Reed, "Using ontology in hierarchical information clustering," , Big Island, HI, United States, 2005, p. 111.

@inproceedings{breaux_using_2005, address = {Big Island, {HI,} United States}, title = {Using ontology in hierarchical information clustering}, abstract = {The tools to analyze and visualize information from multiple, heterogeneous sources have often relied on innovations in statistical methods. The results from purely statistical methods, however, overlook relevant semantic features present within natural language and text-based information. Emerging research in ontology languages (e.g. {RDF,} {RDFS,} {SUO-KIF,} and {OWL)} offers promising avenues for overcoming these limitations by leveraging existing and future libraries of meta-data and semantic mark-up. Using semantic features (e.g. hypernyms, meronyms, synonyms, etc.) encoded in ontology languages, methods such as keyword search and clustering can be augmented to analyze and visualize documents at conceptually richer levels. We present findings from a hierarchical clustering system modified for ontological indexing and run on a topic-centric test collection of documents each with fewer than 200 words. Our findings show that ontologies can impose a complete interpretation or subjective clustering onto a document set that is at least as good as meta-word search.}, publisher = {Institute of Electrical and Electronics Engineers Computer Society, Piscataway, {NJ} 08855-1331, United States}, author = {Travis D. Breaux and Joel W. Reed}, year = {2005}, keywords = {Cluster, Langage naturel, Visualisation de l'information}, pages = {111}, annote = {{\textless}p{\textgreater}1530-1605{\textless}/p{\textgreater}}, annote = {{{\textless}p{\textgreater}Compilation} and indexing terms, Copyright 2007 Elsevier Inc. All rights reserved 05459465175 Hierarchical information clustering Text-based methods Information visualization Hypernyms{\textless}/p{\textgreater}} },
[2005,article] bibtex

J. Zhang and T. Nguyen, "Webstar : a visualization model for hyperlink structures," Information processing \& management, vol. 41, iss. 4, pp. 1003-1018, 2005.

@article{zhang_webstar_2005, title = {Webstar : a visualization model for hyperlink structures}, volume = {41}, issn = {0306-4573}, shorttitle = {Webstar}, url = {http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6VC8-4CBV1PD-2-1&_cdi=5948&_user=789722&_orig=search&_coverDate=07%2F31%2F2005&_sk=999589995&view=c&wchp=dGLbVzz-zSkWz&md5=c892bcc202b488be836902c924d5fcc6&ie=/sdarticle.pdf}, doi = {10.1016/j.ipm.2004.03.005}, abstract = {The authors introduce an information visualization model, {WebStar,} for hyperlink-based information systems. Hyperlinks within a hyperlink-based document can be visualized in a two-dimensional visual space. All links are projected within a display sphere in the visual space. The relationship between a specified central document and its hyperlinked documents is visually presented in the visual space. In addition, users are able to define a group of subjects and to observe relevance between each subject and all hyperlinked documents via movement of that subject around the display sphere center. {WebStar} allows users to dynamically change an interest center during navigation. A retrieval mechanism is developed to control retrieved results in the visual space. Impact of movement of a subject on the visual document distribution is analyzed. An ambiguity problem caused by projection is discussed. Potential applications of this visualization model in information retrieval are included. Future research directions on the topic are addressed.}, number = {4}, journal = {Information processing \& management}, author = {Jin Zhang and Tien Nguyen}, month = jul, year = {2005}, keywords = {Visualisation de l'information, Web}, pages = {1003--1018}, annote = {{{\textless}p{\textgreater}zhangJin2005.pdf{\textless}/p{\textgreater}}} },
[2005,article] bibtex

K. Simon and G. Lausen, "ViPER : augmenting automatic information extraction with visual perceptions," Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 381-388, 2005.

@article{simon_viper_2005, title = {{ViPER} : augmenting automatic information extraction with visual perceptions}, shorttitle = {{ViPER}}, journal = {Proceedings of the 14th {ACM} international conference on Information and knowledge management}, author = {K. Simon and G. Lausen}, year = {2005}, keywords = {Extraction d'information, Visualisation de l'information}, pages = {381--388} },
[2005,book] bibtex

Knowledge and information visualization : searching for synergies, Sigmar-Olaf and Keller, T., Eds., Berlin: Springer, 2005.

@book{tergan_knowledge_2005, address = {Berlin}, series = {Lecture Notes in Computer Science; 3426}, title = {Knowledge and information visualization : searching for synergies}, isbn = {3-540-26921-5}, url = {http://www.springerlink.com/content/ylq6yngae13w/?p=ced1a66ae2634482aab3079c8fbd6a98&pi=1858}, publisher = {Springer}, editor = {{Sigmar-Olaf} Tergan and Tanja Keller}, year = {2005}, keywords = {Visualisation de l'information}
[2004,inproceedings] bibtex

C. Plaisant, "The challenge of information visualization evaluation," in Proceedings of the working conference on advanced visual interfaces, Gallipoli, Italy, 2004, pp. 109-116.

@inproceedings{plaisant_challenge_2004, address = {Gallipoli, Italy}, series = {Improving visualization}, title = {The challenge of information visualization evaluation}, isbn = {1-58113-867-9}, url = {http://portal.acm.org/ft_gateway.cfm?id=989880&type=pdf&coll=GUIDE&dl=GUIDE&CFID=7424735&CFTOKEN=18125341}, doi = {10.1145/989863.989880}, abstract = {As the field of information visualization matures, the tools and ideas described in our research publications are reaching users. The reports of usability studies and controlled experiments are helpful to understand the potential and limitations of our tools, but we need to consider other evaluation approaches that take into account the long exploratory nature of users tasks, the value of potential discoveries or the benefits of overall awareness. We need better metrics and benchmark repositories to compare tools, and we should also seek reports of successful adoption and demonstrated utility.}, booktitle = {Proceedings of the working conference on advanced visual interfaces}, publisher = {{ACM}}, author = {Catherine Plaisant}, year = {2004}, keywords = {Visualisation de l'information}, pages = {109--116}, annote = {{{\textless}p{\textgreater}plaisantCatherine2004.pdf{\textless}/p{\textgreater}}} },
[2004,techreport] bibtex

M. Rasmussen and G. Karypis, "gCLUTO – an interactive clustering, visualization, and analysis system," Department od computer science and Engineering, University of Minnesota, Technical report 04-021, 2004.

@techreport{rasmussen_gcluto_2004, type = {Technical report}, title = {{gCLUTO} - an interactive clustering, visualization, and analysis system}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.1216&rep=rep1&type=pdf}, number = {04-021}, institution = {Department od computer science and Engineering, University of Minnesota}, author = {Matt Rasmussen and George Karypis}, year = {2004}, keywords = {Cluster, Fouille de texte, Visualisation de l'information}, annote = {{{\textless}p{\textgreater}rasmussenMatt2004.pdf{\textless}/p{\textgreater}}} },
[2003,article] bibtex

C. Friedman, H. Liu, and L. Shagina, "A vocabulary development and visualization tool based on natural language processing and the mining of textual patient reports," Journal of Biomedical Informatics, vol. 36, iss. 3, pp. 189-201, 2003.

@article{friedman_vocabulary_2003, title = {A vocabulary development and visualization tool based on natural language processing and the mining of textual patient reports}, volume = {36}, abstract = {Medical terminologies are critical for automated healthcare systems. Some terminologies, such as the {UMLS} and {SNOMED} are comprehensive, whereas others specialize in limited domains (i.e., {BIRADS)} or are developed for specific applications. An important feature of a terminology is comprehensive coverage of relevant clinical terms and ease of use by users, which include computerized applications. We have developed a method for facilitating vocabulary development and maintenance that is based on utilization of natural language processing to mine large collections of clinical reports in order to obtain information on terminology as expressed by physicians. Once the reports are processed and the terms structured and collected into an {XML} representational schema, it is possible to determine information about terms, such as frequency of occurrence, compositionality, relations to other terms (such as modifiers), and correspondence to a controlled vocabulary. This paper describes the method and discusses how it can be used as a tool to help vocabulary builders navigate through the terms physicians use, visualize their relations to other terms via a flexible viewer, and determine their correspondence to a controlled vocabulary.}, number = {3}, journal = {Journal of Biomedical Informatics}, author = {C. Friedman and H. Liu and L. Shagina}, year = {2003}, keywords = {Fouille de texte, Visualisation de l'information}, pages = {189--201}, annote = {{{\textless}p{\textgreater}Terminologie} Biomedical{\textless}/p{\textgreater}} },
[2003,techreport] bibtex

Jean-Daniel, "The InfoVis toolkit," Institut national de recherche en informatique et en automatique (INRIA), Text.TechReport 4818, 2003.

@techreport{fekete_infovis_2003, type = {{Text.TechReport}}, title = {The {InfoVis} toolkit}, url = {ftp://ftp.inria.fr/INRIA/publication/publi-ps-gz/RR/RR-4818.ps.gz}, abstract = {This report presents the {InfoVis} Toolkit, designed to support the creation, extension and integration of advanced {2D} Information Visualization components into interactive Java Swing applications. The {InfoVis} Toolkit provides specific data structures to achieve a fast action/feedback loop required by dynamic queries. It comes with a large set of components such as range sliders and tailored control panels required to control and configure the visualizations. These components are integrated into a coherent framework that simplifies the management of rich data structures and the design and extension of visualizations. Supported data structures currently include tables, trees and graphs. Supported visualizations include scatter plots, time series, Treemaps, node-link diagrams for trees and graphs and adjacency matrix for graphs. All visualizations can use fisheye lenses and dynamic labeling. The {InfoVis} Toolkit supports hardware acceleration when available through {Agile2D,} an implementation of the Java Graphics {API} based on {OpenGL,} achieving speedups of 10 to 60 times. The report also shows how new visualizations can be added and extended to become components, enriching visualizations as well as general applications.}, number = {4818}, institution = {Institut national de recherche en informatique et en automatique {(INRIA)}}, author = {{Jean-Daniel} Fekete}, month = may, year = {2003}, note = {Fekete, {Jean-Daniel}}, keywords = {Visualisation de l'information}, pages = {15 p.}, annote = {{{\textless}p{\textgreater}feketeJean-Daniel2003.pdf{\textless}/p{\textgreater}}} },
[2003,misc] bibtex

M. A. Hearst, Information visualization : principles, promise, and pragmaticsFort Lauderdale, Floride: , 2003.

@misc{hearst_information_2003, address = {Fort Lauderdale, Floride}, type = {{PowerPoint}}, title = {Information visualization : principles, promise, and pragmatics}, url = {bailando.sims.berkeley.edu/talks/chi03-tutorial.ppt}, author = {Marti A. Hearst}, month = apr, year = {2003}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hearstMarti2003.ppt{\textless}/p{\textgreater}}} },
[2003,article] bibtex

R. Cole, "Document retrieval for e-mail search and discovery using formal concept analysis," Applied Artificial Intelligence, vol. 17, iss. 3, pp. 257-280, 2003.

@article{cole_document_2003, title = {Document retrieval for e-mail search and discovery using formal concept analysis}, volume = {17}, url = {http://www.ingentaconnect.com/content/tandf/uaai/2003/00000017/00000003/art00005}, abstract = {This paper discusses a document discovery tool based on Conceptual Clustering by Formal Concept Analysis. The program allows users to navigate e-mail using a visual lattice metaphor rather than a tree. It implements a virtual file structure over e-mail where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in e-mail discovery. The system described provides more flexibility in retrieving stored e-mails than what is normally available ine-mail clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems and aid knowledge discovery in document collections.}, number = {3}, journal = {Applied Artificial Intelligence}, author = {R. Cole}, year = {2003}, keywords = {Visualisation de l'information}, pages = {257--280} },
[2002,misc] bibtex

M. Hearst, Brushing and linking with EDV, 2002.

@misc{hearst_brushing_2002, type = {{PowerPoint}}, title = {Brushing and linking with {EDV}}, url = {http://www2.sims.berkeley.edu/courses/is247/s02/lectures/Lecture3.ppt}, author = {Marti Hearst}, month = feb, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hearstMarti2002\_3.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

J. Reffell, M. Aydelott, and Jean-Anne, Visualization of networks, 2002.

@misc{reffell_visualization_2002, type = {{PowerPoint}}, title = {Visualization of networks}, url = {http://dream.sims.berkeley.edu/newshound/infovis/networks/networks.ppt}, author = {James Reffell and Moryma Aydelott and {Jean-Anne} Fitzpatrick}, month = apr, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}reffellJames2002.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

S. Eklund, Empirical evaluation of visualization, 2002.

@misc{eklund_empirical_2002, type = {{PowerPoint}}, title = {Empirical evaluation of visualization}, url = {http://www2.sims.berkeley.edu/courses/is247/s02/lectures/empirical_eval_presentation.ppt}, author = {Susanne Eklund}, month = mar, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}eklundSusanne2002.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

M. Law and V. Petras, Conversations, 2002.

@misc{law_conversations_2002, type = {{PowerPoint}}, title = {Conversations}, url = {http://www.sims.berkeley.edu/academics/courses/is247/s02/lectures/Conversations.ppt}, abstract = {Seminar discussion slides presenting experimental techniques -- including related technological and social structures -- used for visualizing conversations. Specific focus given to Comic Chat (a Microsoft product no longer in development, but still quite widely used), Chat Circles (an experimental tool created at the {MIT} Media Lab) and former {SIMS} professor Warren Sack's Conversation Map (first developed at {MIT).}}, author = {Maggie Law and Vivien Petras}, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}maggieLaw2002.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

M. Hearst, Introduction to information visualization, 2002.

@misc{hearst_introduction_2002, type = {{PowerPoint}}, title = {Introduction to information visualization}, url = {http://www2.sims.berkeley.edu/courses/is247/s02/lectures/Lecture1.ppt}, author = {Marti Hearst}, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hearstMarti2002\_1.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

S. Waterson, Brushing, linking \& interactive querying, 2002.

@misc{waterson_brushing_2002, type = {{PowerPoint}}, title = {Brushing, linking \& interactive querying}, author = {Sarah Waterson}, month = feb, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}watersonSarah2002.ppt{\textless}/p{\textgreater}}} },
[2002,inproceedings] bibtex

C. Y. Chung, R. Lieu, J. Liu, A. Luk, J. Mao, and P. Raghavan, "Thematic mapping : from unstructured documents to taxonomies," in Proceedings of the eleventh international conference on Information and knowledge management, McLean, Virginia, USA, 2002, pp. 608-610.

@inproceedings{chung_thematic_2002, address = {{McLean,} Virginia, {USA}}, series = {Industry session 1 : knowledge management and semantics}, title = {Thematic mapping : from unstructured documents to taxonomies}, isbn = {1-58113-492-4}, url = {http://portal.acm.org/ft_gateway.cfm?id=584892&type=pdf&coll=GUIDE&dl=GUIDE&CFID=7874383&CFTOKEN=23247601}, doi = {10.1145/584792.584892}, abstract = {Verity Inc. has developed a comprehensive suite of tools for accurately and efficiently organizing enterprise content which involves four basic steps: (i) creating taxonomies, (ii) building classification models, (iii) populating taxonomies with documents, and (iv) deploying populated taxonomies in enterprise portals. A taxonomy is a hierarchical representation of categories. A taxonomy provides a navigation structure for exploring and understanding the underlying corpus without sifting through a huge volume of documents. Thematic Mapping automatically discovers a concept tree from a corpus of unstructured documents and assigns meaningful labels to concepts based on a semantic network. Integrating with Verity Intelligent Classifier's user-friendly {GUI,} a user can drill down a concept tree for navigation, perform a conceptual search to retrieve documents pertaining to a concept, build a taxonomy from the concept tree, as well as edit a taxonomy to tailor it into various views (customized taxonomies) of the same corpus. Classification rules can be generated automatically from concepts. These classification rules can be used for populating documents into the taxonomy.}, booktitle = {Proceedings of the eleventh international conference on Information and knowledge management}, publisher = {{ACM}}, author = {Christina Yip Chung and Raymond Lieu and Jinhui Liu and Alpha Luk and Jianchang Mao and Prabhakar Raghavan}, year = {2002}, keywords = {Cluster, Visualisation de l'information}, pages = {608--610}, annote = {{{\textless}p{\textgreater}chungChristina2002.pdf{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

C. Rixford, Trees, hierarchies, and multi-trees, 2002.

@misc{rixford_trees_2002, title = {Trees, hierarchies, and multi-trees}, url = {http://www.sims.berkeley.edu/courses/is247/s02/lectures/tree_visualization.ppt}, author = {Craig Rixford}, month = apr, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}rixfordCraig2002.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

M. Hearst, Visualisation in text and research, 2002.

@misc{hearst_visualisation_2002, type = {{PowerPoint}}, title = {Visualisation in text and research}, url = {http://www2.sims.berkeley.edu/courses/is247/s02/lectures/TextAndSearch.ppt}, author = {Marti Hearst}, month = mar, year = {2002}, keywords = {Fouille de texte, Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hearstMarti2002.ppt{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

M. Hearst, Principles of information visualization, 2002.

@misc{hearst_principles_2002, type = {{PowerPoint}}, title = {Principles of information visualization}, url = {http://www2.sims.berkeley.edu/courses/is247/s02/lectures/Lecture2.ppt}, author = {Marti Hearst}, month = feb, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hearstMarti2002\_2.ppt{\textless}/p{\textgreater}}} },
[2002,article] bibtex

A. Kabán and M. A. Girolami, "A dynamic probabilistic model to visualise topic evolution in text streams," Journal of intelligent information systems, vol. 18, iss. 2-3, pp. 107-125, 2002.

@article{kabn_dynamic_2002, title = {A dynamic probabilistic model to visualise topic evolution in text streams}, volume = {18}, url = {http://dx.doi.org/10.1023/A:1013673310093}, doi = {10.1023/A:1013673310093}, abstract = {We propose a novel probabilistic method, based on latent variable models, for unsupervised topographic visualisation of dynamically evolving, coherent textual information. This can be seen as a complementary tool for topic detection and tracking applications. This is achieved by the exploitation of the a priori domain knowledge available, that there are relatively homogeneous temporal segments in the data stream. In a different manner from topographical techniques previously utilized for static text collections, the topography is an outcome of the coherence in time of the data stream in the proposed model. Simulation results on both toy-data settings and an actual application on Internet chat line discussion analysis is presented by way of demonstration.}, number = {2-3}, journal = {Journal of intelligent information systems}, author = {Ata Kabán and Mark A. Girolami}, year = {2002}, keywords = {Visualisation de l'information}, pages = {107--125}, annote = {{{\textless}p{\textgreater}kabanAta2002.pdf{\textless}/p{\textgreater}}} },
[2002,article] bibtex

S. Havre, E. Hetzler, P. Whitney, and L. Nowell, "ThemeRiver : visualizing thematic changes in large document collections," IEEE transactions on visualization and computer graphics, vol. 8, iss. 1, pp. 9-20, 2002.

@article{havre_themeriver_2002, title = {{ThemeRiver} : visualizing thematic changes in large document collections}, volume = {8}, issn = {1077-2626}, shorttitle = {{ThemeRiver}}, abstract = {The {ThemeRiver} visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time line and corresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time line, selected thematic content, and thematic strength are indicated by the river's directed flow, composition, and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored ¿currents¿ flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme.}, number = {1}, journal = {{IEEE} transactions on visualization and computer graphics}, author = {Susan Havre and Elizabeth Hetzler and Paul Whitney and Lucy Nowell}, year = {2002}, keywords = {Visualisation de l'information}, pages = {9--20}, annote = {{{\textless}p{\textgreater}havreSusan2002.pdf{\textless}/p{\textgreater}}} },
[2002,misc] bibtex

K. Fishkin, Magic Lenses for interactive database visualization, 2002.

@misc{fishkin_magic_2002, type = {{PowerPoint}}, title = {Magic Lenses for interactive database visualization}, author = {Ken Fishkin}, month = nov, year = {2002}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}fishkinKen2002.ppt{\textless}/p{\textgreater}}} },
[2001,book] bibtex

T. Kohonen, Self-organizing maps, 3rd ed. ed., Berlin: Springer, 2001.

@book{kohonen_self-organizing_2001, address = {Berlin}, edition = {3rd ed.}, title = {Self-organizing maps}, isbn = {3540679219}, abstract = {The {Self-Organizing} Map {(SOM),} with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. Many fields of science have adopted the {SOM} as a standard analytical tool: in statistics,signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. A new area is organization of very large document collections. The {SOM} is also one of the most realistic models of the biological brain functions. This new edition includes a survey of over 2000 contemporary studies to cover the newest results; the case examples were provided with detailed formulae, illustrations and tables; a new chapter on software tools for {SOM} was written, other chapters were extended or reorganized.}, publisher = {Springer}, author = {Teuvo Kohonen}, year = {2001}, keywords = {Apprentissage machine, Catégorisation, Réseau de neurones, Visualisation de l'information} },
[2001,book] bibtex

U. Fayyad, G. G. Grinstein, and A. Wierse, Information visualization in data mining and knowledge discovery, San Francisco: Morgan Kaufmann, 2001.

@book{fayyad_information_2001, address = {San Francisco}, title = {Information visualization in data mining and knowledge discovery}, publisher = {Morgan Kaufmann}, author = {Usama Fayyad and Georges G. Grinstein and Andreas Wierse}, year = {2001}, keywords = {Fouille de donnée, Visualisation de l'information} },
[2001,techreport] bibtex

B. Scneiderman, "Inventing discovery tools : combining information visualization with data mining," Institute for systems research2001.

@techreport{scneiderman_inventing_2001, title = {Inventing discovery tools : combining information visualization with data mining}, abstract = {The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. This paper discusses two issues that influence design of discovery tools: statistical algorithms vs. visual data presentation, and hypothesis testing vs. exploratory data analysis. I claim that a combined approach could lead to novel discovery tools that preserve user control, enable more effective exploration, and promote responsibility.}, institution = {Institute for systems research}, author = {Ben Scneiderman}, year = {2001}, keywords = {Fouille de donnée, Visualisation de l'information}, pages = {17--28}, annote = {{{\textless}p{\textgreater}scneidermanBen2001.pdf{\textless}/p{\textgreater}}} },
[2000,phdthesis] bibtex

C. Eliasmith, "How neurons mean : a neurocomputational theory of representational content," PhD Thesis , 2000.

@phdthesis{eliasmith_neurons_2000, title = {How neurons mean : a neurocomputational theory of representational content}, url = {http://watarts.uwaterloo.ca/~celiasmi/Papers/Eliasmith.2000.How%20neurons%20mean.PhD.pdf}, school = {Université de Washington à {Saint-Louis,} {St-Louis,} Missouri, {USA}}, author = {Chris Eliasmith}, month = may, year = {2000}, keywords = {Réseau de neurones, Visualisation de l'information}, pages = {92 p.}, annote = {{{\textless}p{\textgreater}eliasmithChris2000.pdf{\textless}/p{\textgreater}}} },
[2000,book] bibtex

R. Spence, Information visualization, Boston: Addison-Wesley, 2000.

@book{spence_information_2000, address = {Boston}, title = {Information visualization}, publisher = {{Addison-Wesley}}, author = {Robert Spence}, year = {2000}, keywords = {Visualisation de l'information} },
[2000,inproceedings] bibtex

S. Havre, B. Hetzler, and L. Nowell, "ThemeRiver : visualizing theme changes over time," in IEEE symposium on information visualization, 2000. InfoVis 2000., Salt Lake City, UT, USA, 2000, pp. 115-123.

@inproceedings{havre_themeriver_2000, address = {Salt Lake City, {UT,} {USA}}, title = {{ThemeRiver} : visualizing theme changes over time}, isbn = {0-7695-0804-9}, shorttitle = {{ThemeRiver}}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=885098&isnumber=19138}, doi = {10.1109/INFVIS.2000.885098}, abstract = {{ThemeRiverTM} is a prototype system that visualizes thematic variations over time within a large collection of documents. The “river” flows from left to right through time, changing width to depict changes in thematic strength of temporally associated documents. Colored “currents” flowing within the river narrow or widen to indicate decreases or increases in the strength of an individual topic or a group of topics in the associated documents. The river is shown within the context of a timeline and a corresponding textual presentation of external events}, booktitle = {{IEEE} symposium on information visualization, 2000. {InfoVis} 2000.}, author = {Susan Havre and Beth Hetzler and Lucy Nowell}, year = {2000}, keywords = {Visualisation de l'information}, pages = {115--123}, annote = {{{\textless}p{\textgreater}havreSusan2000.pdf{\textless}/p{\textgreater}}} },
[2000,misc] bibtex

M. Hearst, User interfaces and visualization for information access, 2000.

@misc{hearst_user_2000, type = {{PowerPoint} {(Tutorial)}}, title = {User interfaces and visualization for information access}, url = {http://bailando.sims.berkeley.edu/talks/sigir-viz-tutorial-2000.ppt}, author = {Marti Hearst}, year = {2000}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hearstMarti2000.ppt{\textless}/p{\textgreater}}} },
[1999,article] bibtex

P. C. Wong, "Visual data mining," Computer graphics and applications, IEEE, vol. 19, iss. 5, pp. 20-21, 1999.

@article{wong_visual_1999, title = {Visual data mining}, volume = {19}, issn = {0272-1716}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=788794&isnumber=17095}, doi = {10.1109/MCG.1999.788794}, number = {5}, journal = {Computer graphics and applications, {IEEE}}, author = {Pak Chung Wong}, year = {1999}, keywords = {Fouille de donnée, Visualisation de l'information}, pages = {20--21}, annote = {{{\textless}p{\textgreater}wongPak1999.pdf{\textless}/p{\textgreater}}} },
[1998,inproceedings] bibtex

N. E. Miller, P. C. Wong, M. Brewster, and H. Foote, "TOPIC ISLANDS : a wavelet-based text visualization system," in Proceedings of the conference on visualization ‘98, Research triangle park, N.C., 1998, pp. 189-196.

@inproceedings{miller_topic_1998, address = {Research triangle park, {N.C.}}, title = {{TOPIC} {ISLANDS} : a wavelet-based text visualization system}, isbn = {1-58113-106-2}, url = {http://portal.acm.org/ft_gateway.cfm?id=288247&type=pdf&coll=portal&dl=ACM&CFID=7894839&CFTOKEN=49282809}, booktitle = {Proceedings of the conference on visualization '98}, publisher = {{IEEE} computer society press}, author = {Nancy E. Miller and Pak Chung Wong and Mary Brewster and Harlan Foote}, year = {1998}, keywords = {Recherche d'information, Visualisation de l'information}, pages = {189--196}, annote = {{{\textless}p{\textgreater}millerNancy1998.pdf{\textless}/p{\textgreater}}} },
[1998,article] bibtex

S. Kaski, J. Kangasz, and T. Kohoneny, "Bibliography of self-organizing map (SOM) papers : 1981-1997," Neutral computing surveys, vol. 1, iss. 4, pp. 102-350, 1998.

@article{kaski_bibliography_1998, title = {Bibliography of self-organizing map {(SOM)} papers : 1981-1997}, volume = {1}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.50.5846}, doi = {10.1.1.50.5846}, abstract = {The {Self-Organizing} Map {(SOM)} algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of fields. The {SOM} and a variant of it, the {LVQ,} have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within fields ranging from engineering sciences to medicine, biology, and economics. We have collected a comprehensive list of 3343 scientific papers that use the algorithms, have benefited from them, or contain analyses of them. The list is intended to serve as a source for literature surveys. We have provided both a thematic and a keyword index to help finding articles of interest.}, number = {4}, journal = {Neutral computing surveys}, author = {Samuel Kaski and Jari Kangasz and Teuvo Kohoneny}, year = {1998}, keywords = {Visualisation de l'information}, pages = {102--350}, annote = {{{\textless}p{\textgreater}kaskiSamuel1998.pdf{\textless}/p{\textgreater}}} },
[1998,inproceedings] bibtex

D. Landau, R. Feldman, Y. Aumann, M. Fresko, Y. Lindell, O. Lipshtat, and O. Zamir, "TextVis : an integrated visual environment for text mining," in Principles of data mining and knowledge discovery : second European symposium, PKDD ’98 Nantes, France, september 23–26, 1998 : proceedings, Berlin; Heidelberg, 1998, pp. 56-64.

@inproceedings{landau_textvis_1998, address = {Berlin; Heidelberg}, series = {Lecture notes in computer science; 1510. Lecture notes in artificial intelligence}, title = {{TextVis} : an integrated visual environment for text mining}, isbn = {978-3-540-65068-3}, url = {http://dx.doi.org/10.1007/BFb0094805}, abstract = {{TextVis} is a visual data mining system for document collections. Such a collection represents an application domain, and the primary goal of the system is to derive patterns that provide knowledge about this domain. Additionally, the derived patterns can be used to browse the collection. {TextVis} takes a multi-strategy approach to text mining, and enables defining complex analysis schemas from basic components, provided by the system. An analysis schema is constructed by dragging functional icons from a tool-pallette onto the workspace and connecting them according to the desired flow of information. The system provides a large collection of basic analysis tools, including: frequent sets, associations, concept distributions, and concept correlations. The discovered patterns are presented in a visual interface allowing the user to operate on the results, and to access the associated documents. {TextVis} is a complete text mining system which uses agent technology to access various online information sources, text preprocessing tools to extract relevant information from the documents, a variety of data mining algorithms, and a set of visual browsers to view the results. This paper provides an overview on the {TextVis} system. We describe the system’s architecture, the various tools, and discuss the advantages of our visual environment for mining large document collections.}, booktitle = {Principles of data mining and knowledge discovery : second European symposium, {PKDD} ’98 Nantes, France, september 23–26, 1998 : proceedings}, publisher = {Springer}, author = {David Landau and Ronen Feldman and Yonatan Aumann and Moshe Fresko and Yehuda Lindell and Orly Lipshtat and Oren Zamir}, year = {1998}, keywords = {Fouille de texte, Visualisation de l'information}, pages = {56--64} },
[1997,article] bibtex

T. Honkela, "Self-organizing maps in natural language processing," , 1997.

@article{honkela_self-organizing_1997, title = {Self-organizing maps in natural language processing}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.6204&rep=rep1&type=pdf}, doi = {10.1.1.51.6204}, author = {Timo Honkela}, year = {1997}, keywords = {Langage naturel, Réseau de neurones, Visualisation de l'information}, annote = {{{\textless}p{\textgreater}honkelaTimo1997.pdf{\textless}/p{\textgreater}}} },
[1996,incollection] bibtex

N. J. Nilsson, "Decision trees." , 1996, pp. 81-96.

@incollection{nilsson_decision_1996, title = {Decision trees}, url = {http://robotics.stanford.edu/~nilsson/MLDraftBook/ch6-ml.pdf}, booktitle = {Introduction to machine learning}, author = {Nils J. Nilsson}, year = {1996}, keywords = {Visualisation de l'information}, pages = {81--96}, annote = {{{\textless}p{\textgreater}nilssonNils1996\_3.pdf{\textless}/p{\textgreater}}} },
[1996,article] bibtex

K. Lagus, T. Honkela, S. Kaski, and T. Kohonen, "Self-organizing maps of document collections : a new approach to interactive exploration," , pp. 238-243, 1996.

@article{lagus_self-organizing_1996, title = {Self-organizing maps of document collections : a new approach to interactive exploration}, shorttitle = {Self-organizing maps of document collections}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.7732&rep=rep1&type=pdf}, doi = {10.1.1.50.7732}, abstract = {Powerful methods for interactive exploration and search from collections of free-form textual documents are needed to manage the ever-increasing flood of digital information. In this article we present a method, {WEBSOM,} for automatic organization of full-text document collections using the self-organizing map {(SOM)} algorithm. The document collection is ordered onto a map in an unsupervised manner utilizing statistical information of short word contexts. The resulting ordered map where similar documents lie near each other thus presents a general view of the document space. With the aid of a suitable {(WWWbased)} interface, documents in interesting areas of the map can be browsed. The browsing can also be interactively extended to related topics, which appear in nearby areas on the map. Along with the method we present a case study of its use.}, author = {Krista Lagus and Timo Honkela and Samuel Kaski and Teuvo Kohonen}, year = {1996}, keywords = {Recherche d'information, Réseau de neurones, Visualisation de l'information}, pages = {238--243}, annote = {{{\textless}p{\textgreater}langusKrista1996.pdf{\textless}/p{\textgreater}}} },
[1996,inproceedings] bibtex

S. Kaski and K. Lagus, "Comparing self-organizing maps," in Artificial neural networks : ICANN 96 : 1996 international conference, Bochum, Germany, July 16-19, 1996 : proceedings, Berlin ; New York, 1996, pp. 809-814.

@inproceedings{kaski_comparing_1996, address = {Berlin ; New York}, series = {Lecture notes in computer science; 1112}, title = {Comparing self-organizing maps}, isbn = {3540615105}, abstract = {In exploratory analysis of high-dimensional data the selforganizing map can be used to illustrate relations between the data items. We have developed two measures for comparing how different maps represent these relations. The other combines an index of discontinuities in the mapping from the input data set to the map grid with an index of the accuracy with which the map represents the data set. This measure can be used for determining the goodness of single maps. The other measure has been used to directly compare how similarly two maps represent relations between data items. Such a measure of the dissimilarity of maps is useful, e.g., for analyzing the sensitivity of maps to variations in their inputs or in the learning process. Also the similarity of two data sets can be compared indirectly by comparing the maps that represent them.}, booktitle = {Artificial neural networks : {ICANN} 96 : 1996 international conference, Bochum, Germany, July 16-19, 1996 : proceedings}, publisher = {Springer}, author = {Samuel Kaski and Krista Lagus}, year = {1996}, keywords = {Visualisation de l'information}, pages = {809--814}, annote = {{{\textless}p{\textgreater}kaskiSamuel1996.pdf{\textless}/p{\textgreater}}} },
[1994,misc] bibtex

S. K. Card, P. Pirolli, and J. D. Mackinlay, The cost-of-knowledge characteristic function : diaplay evaluation for direct-walk dynamic information visualizations, 1994.

@misc{card_cost-of-knowledge_1994, type = {{PowerPoint}}, title = {The cost-of-knowledge characteristic function : diaplay evaluation for direct-walk dynamic information visualizations}, author = {Stuart K. Card and Peter Pirolli and Jock D. Mackinlay}, year = {1994}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}cardStuart1994.ppt{\textless}/p{\textgreater}}} },
[,article] bibtex

S. Havre, B. Hetzier, and L. Nowell, "ThemeRiver TM * : In search of trends, patterns, and relationships."

@article{havre_themeriver_????, title = {{ThemeRiver} {TM} * : In search of trends, patterns, and relationships}, shorttitle = {{ThemeRiver} {TM} *}, url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.39.2977&rep=rep1&type=pdf}, doi = {10.1.1.39.2977}, abstract = {{ThemeRiver} ™ is a prototype system that visualizes thematic variations over time across a collection of documents. The “river ” flows through time, changing width to depict changes in the thematic strength of documents temporally collocated. Themes or topics are represented as colored “currents” flowing within the river that narrow or widen to indicate decreases or increases in the strength of a topic in associated documents at a specific point in time. The river is shown within the context of a timeline and a corresponding textual presentation of external events. Keywords visualization, visualization metaphors, trend analysis, timeline}, author = {Susan Havre and Beth Hetzier and Lucy Nowell}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}havreSusan.pdf{\textless}/p{\textgreater}}} },
[,misc] bibtex

S. Obromsook and L. Harjono, Animation.

@misc{obromsook_animation_????, type = {{PowerPoint}}, title = {Animation}, author = {Saifon Obromsook and Linda Harjono}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}obromsookSaifon.ppt{\textless}/p{\textgreater}}} },
[,misc] bibtex

S. Hornung and L. Zagreus, Zooming, focus + context, and distortion.

@misc{hornung_zooming_????, title = {Zooming, focus + context, and distortion}, author = {Stephanie Hornung and Leah Zagreus}, keywords = {Visualisation de l'information}, annote = {{{\textless}p{\textgreater}hornungStephanie.ppt{\textless}/p{\textgreater}}} },
[,misc] bibtex

D. McQuerry, Visual text analysis.

@misc{mcquerry_visual_????, title = {Visual text analysis}, author = {Dennis {McQuerry}}, keywords = {Fouille de texte, Visualisation de l'information}, annote = {{{\textless}p{\textgreater}mcquerryDennis.pdf{\textless}/p{\textgreater}}} },