. .. Before-remove,

. J-k-=-j-k-implies-?i-?-i-k-,-c-i-?-j-k-=-j-k-and-?i-?-i-k-,-c-i-?-j-k-=-j-k, Axiom 1 implies i ? I k and i ? I k and thus I k = I k

. I-k-?-i-k-?-j-k-?-j-k-(¬m and . Ic, Prior: I k ? {i} I k ?, J k ? J k (?), p.1

, Lemma 1 results in a contradiction: I k ? I k ?{i}

(. I-k-=-i-k-?-j-k-?-j-k and . Ic-?-rem, Prior: I k ? I k ? {i} ?

. I-k-?-i-k-?-j-k-?-j-k-(m-ic-?-keep, Prior: I k ? I k ? {i} ?

. I-k-i-k-?-j-k-?-j-k, ?) Prior: I k ? {i} I k ? J k ? J k (?) Lemma 1 results in a contradiction: I k ? I k ? {i}

. I-k-?-i-k-?-j-k-?-j-k-(m-ic, Prior: I k ? {i} I k ? J k ? J k (?) Lemma 1 results in a contradiction: I k ?{i} ? I k

. I-k-=-i-k-?-j-k-?-j-k-(¬m and . Ic, Prior: I k ? I k ? {i} ?

. I-k-?-i-k-?-j-k-?-j-k-(¬m and . Ic, Prior: I k ? I k ? {i} ?

. I-k-i-k-?-j-k-?-j-k, ?) Prior: I k ? {i} I k ? J k ? J k (?) Lemma 1 results in a contradiction: I k ? {i} ? I k

. I-k-?-i-k-?-j-k-j-k, ? can be ignored) Prior: I k ? {i} I k ? J k J k (M IC) We can show with Axiome 2 that ?B k" : I k" = I k ? J k" = J k ? J k , that meets the condition to remove B k . But, Lemma 2 results in a contradiction: J k ? J k

. I-k-=-i-k-?-j-k-j-k, ?) Prior: I k ? I k ? {i} ? J k J k (?) Lemma 2 results in a contradiction: J k ? J k

. I-k-?-i-k-?-j-k-j-k, ?) Prior: I k ? I k ? {i} ? J k J k (?) Lemma 2 results in a contradiction: J k ? J k

. I-k-i-k-?-j-k-j-k-(m-ic-?-keep, Prior: I k ? {i} I k

, Analyse exploratoire de corpus textuels pour le journalisme d'investigation, Nicolas Médoc, Mohammad Ghoniem and Mohamed Nadif, EGC 2017, vol.33, pp.477-480

, Nicolas Médoc, Mohammad Ghoniem and Mohamed Nadif, Actes de la 28ième conférence francophone sur l'Interaction Homme-Machine (IHM '16, pp.103-114

, Mohammad Ghoniem and Mohamed Nadif, ? Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering, Nicolas Médoc, vol.9853, pp.59-62

, ? Vers une approche Visual analytics pour explorer les variantes de sujet d'un corpus, Nicolas Médoc, Mohammad Ghoniem and Mohamed Nadif, EGC 2016, pp.539-540

, Under revision papers ? Things that Matter for Topic Comprehension: Size and Topic Model, Nicolas Mé-doc, Mohammad Ghoniem and Mohamed Nadif. Selected for the fast track review process of, ? Visual Analytics of Text Streams Through Multiple Dynamic Frequency Matrices, Nicolas Médoc, pp.381-382

C. C. Aggarwal and C. X. Zhai, Mining text data, vol.15, 2012.

C. C. Aggarwal and C. X. Zhai, A Survey of Text Clustering Algorithms, Mining Text Data, pp.77-128, 2012.

C. C. Aggarwal and C. K. Reddy, Data Clustering: Algorithms and Applications, p.18, 2013.

M. Ailem, F. Role, and M. Nadif, Co-clustering Document-term Matrices by Direct Maximization of Graph Modularity, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, pp.1807-1810, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01306473

M. Ailem, F. Role, and M. Nadif, Graph modularity maximization as an effective method for co-clustering text data, Knowledge-Based Systems, vol.109, pp.160-173, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01343616

]. D. Aldous, Exchangeability and related topics, École d'été de Probabilités de Saint-Flour XIII-1983, pp.1-198, 1985.

A. B. Alencar, M. C. De-oliveira, and F. V. Paulovich, Seeing beyond reading: a survey on visual text analytics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.2, p.32, 2012.

N. Aletras and M. Stevenson, Evaluating topic coherence using distributional semantics, Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013)-Long Papers, vol.30, pp.13-22, 2013.

E. Alexander, J. Kohlmann, R. Valenza, M. Witmore, M. Gleisher et al., Serendip: Topic model-driven visual exploration of text corpora, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), pp.1-1, 2014.

D. Archambault, D. Greene, and P. Cunningham, TwitterCrowds: Techniques for Exploring Topic and Sentiment in Microblogging Data. CoRR, 2013.

D. Archambault and H. C. Purchase, On the effective visualisation of dynamic attribute cascades, Information Visualization, vol.15, issue.1, p.42, 2016.

B. Bach, E. Pietriga, and J. D. Fekete, GraphDiaries: Animated Transitions andTemporal Navigation for Dynamic Networks, IEEE Transactions on Visualization and Computer Graphics, vol.20, issue.5, p.42, 2014.

B. Bach, P. Dragicevic, D. Archambault, C. Hurter, and S. Carpendale, A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes, Computer Graphics Forum, p.41, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01303506

S. Barkow, S. Bleuler, A. Preli´cpreli´c, P. Zimmermann, and E. Zitzler, BicAT: a biclustering analysis toolbox, Bioinformatics, vol.22, issue.10, pp.1282-1283, 2006.

]. J. Bertin and J. Bertin, La graphique et le traitement graphique de l'information. Number 91 (084.21) BER, p.31, 1977.

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, the Journal of machine Learning research, vol.3, pp.993-1022, 2003.

M. Bostock and . D3-sunburst, , p.92, 2013.

P. S. Bradley, O. L. Mangasarian, and W. N. Street, Clustering via concave minimization, Advances in neural information processing systems, p.19, 1997.

M. Brehmer and T. Munzner, A Multi-Level Typology of Abstract Visualization Tasks, IEEE TVCG, vol.19, issue.12, pp.2376-2385, 2013.

M. Brehmer, S. Ingram, J. Stray, and T. Munzner, Overview: The Design, Adoption, and Analysis of a Visual Document Mining Tool for Investigative Journalists, IEEE TVCG, vol.20, issue.12, pp.2271-2280, 2014.

C. A. Brewer and . Colorbrewer, Online; accessed 01-jun-2017, p.108, 2017.

D. A. Broniatowski, M. J. Paul, and M. Dredze, National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic, PloS one, vol.8, issue.12, p.83672, 2013.

E. Cambria and B. White, Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article, IEEE Computational Intelligence Magazine, vol.9, issue.2, p.13, 2014.

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational statistics & Data analysis, vol.14, issue.3, pp.315-332, 1992.
URL : https://hal.archives-ouvertes.fr/inria-00075196

J. Chae, D. Thom, Y. Jang, S. Y. Kim, T. Ertl et al., Public behavior response analysis in disaster events utilizing visual analytics of microblog data, Computers & Graphics, vol.38, p.129, 2014.

J. Chang, J. Boydgraber, S. Gerrish, C. Wang, and D. Blei, Reading Tea Leaves: How Humans Interpret Topic Models, Advances in Neural Information Processing Systems, vol.22, p.29, 2009.

H. Cho, I. S. Dhillon, Y. Guan, and S. Sra, Minimum sum-squared residue coclustering of gene expression data, Proceedings of the 2004 SIAM International Conference on Data Mining, p.22, 2004.

J. Choo, C. Lee, C. K. Reddy, and H. Park, UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization, IEEE TVCG, vol.19, issue.12, 1992.

J. Chuang, D. Ramage, C. Manning, and J. Heer, Interpretation and trust: designing model-driven visualizations for text analysis, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '12, vol.48, pp.443-452, 2012.

]. K. Church and P. Hanks, Word Association Norms, Mutual Information, and Lexicography, Comput. Linguist, vol.16, issue.1, p.15, 1990.

C. Collins, F. B. Viegas, and M. Wattenberg, Parallel Tag Clouds to explore and analyze faceted text corpora, IEEE Symposium on Visual Analytics Science and Technology, vol.36, pp.91-98, 2009.

S. P. Crain, K. Zhou, S. Yang, and H. Zha, Dimensionality Reduction and Topic Modeling: From Latent Semantic Indexing to Latent Dirichlet Allocation and Beyond, Mining Text Data, vol.16, pp.129-161, 2012.

S. ]-weiwei-cui, L. Liu, C. Tan, Y. Shi, Z. Song et al., TextFlow: Towards Better Understanding of Evolving Topics in Text, IEEE TVCG, vol.17, issue.12, pp.2412-2421, 2011.

L. J. Deborah, R. Baskaran, and A. Kannan, A Survey on Internal Validity Measure for Cluster Validation, International Journal of Computer Science and Engineering Survey, vol.1, issue.2, p.29, 2010.

S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American Society for Information Science, vol.41, issue.6, p.16, 1990.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the royal statistical society. Series B (methodological), pp.1-38, 1977.

S. Denef, P. S. Bayerl, and N. A. Kaptein, Social Media and the Police: Tweeting Practices of British Police Forces During the, Proc. CHI'13, vol.129, pp.3471-3480, 2011.

I. S. Dhillon, Co-clustering Documents and Words Using Bipartite Spectral Graph Partitioning, Proc. of the 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD '01, p.22, 2001.

I. S. Dhillon and D. S. Modha, Concept Decompositions for Large Sparse Text Data Using Clustering, Machine Learning, vol.42, pp.143-175, 2001.

S. Inderjit, S. Dhillon, D. S. Mallela, and . Modha, Information-theoretic Co-clustering, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, p.22, 2003.

C. Ding, X. He, H. Zha, M. Gu, and H. D. Simon, A min-max cut algorithm for graph partitioning and data clustering, Proceedings IEEE International Conference on, vol.20, pp.107-114, 2001.

A. Don, E. Zheleva, M. Gregory, S. Tarkan, L. Auvil et al., Discovering Interesting Usage Patterns in Text Collections: Integrating Text Mining with Visualization, Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM '07, vol.49, pp.213-222, 2007.

W. Dou, X. Wang, R. Chang, and W. Ribarsky, ParallelTopics: A probabilistic approach to exploring document collections, 2011 IEEE VAST, vol.46, p.67, 2011.

W. Dou, D. Wang, W. Skau, M. X. Ribarsky, and . Zhou, LeadLine: Interactive visual analysis of text data through event identification and exploration, 2012 IEEE VAST, vol.40, pp.93-102, 2012.

W. Dou, L. Yu, X. Wang, Z. Ma, and W. Ribarsky, HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies, IEEE TVCG, vol.19, issue.12, 2002.

S. T. Dumais and J. Nielsen, Automating the Assignment of Submitted Manuscripts to Reviewers, Proceedings of the 15th Annual International ACM SIGIR, SIGIR '92, p.16, 1992.

A. Endert, P. Fiaux, and C. North, Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering, IEEE Transactions on Visualization and Computer Graphics, vol.18, issue.12, pp.2879-2888, 2012.

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Kdd, vol.96, p.19, 1996.

B. S. Everitt, S. Landau, M. Leese, and D. Stahl, Cluster Analysis, p.18, 2011.

J. Fekete and C. Plaisant, Excentric Labeling: Dynamic Neighborhood Labeling for Data Visualization, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '99, vol.109, pp.512-519, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00877395

J. Fekete, N. Wang, and . Dang, Interactive poster: Overlaying graph links on treemaps, Proceedings of the IEEE Symposium on Information Visualization Conference Compendium (InfoVis? 03), p.91, 2003.

P. Fiaux, M. Sun, L. Bradel, C. North, N. Ramakrishnan et al., Bixplorer: Visual Analytics with Biclusters. Computer, vol.46, issue.8, pp.90-94, 2013.

J. Fulda, M. Brehmer, and T. Munzner, TimeLineCurator: Interactive Authoring of Visual Timelines from Unstructured Text, IEEE Transactions on Visualization and Computer Graphics, issue.99, p.35, 2015.

P. Gambette and J. Véronis, Visualising a Text with a Tree Cloud, Classification as a Tool for Research, vol.36, pp.561-569, 2010.
URL : https://hal.archives-ouvertes.fr/lirmm-00373643

Q. Gan, M. Zhu, M. Li, T. Liang, Y. Cao et al., Document visualization: an overview of current research, Wiley Interdisciplinary Reviews: Computational Statistics, vol.6, issue.1, p.32, 2014.

S. Gerrish and D. M. Blei, A language-based approach to measuring scholarly impact, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp.375-382, 2010.

M. Ghoniem, J. Fekete, and P. Castagliola, On the Readability of Graphs Using Node-Link and Matrix-Based Representations: A Controlled Experiment and Statistical Analysis, Information Visualization, vol.4, issue.2, p.53, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00340790

M. Ghoniem, D. Luo, J. Yang, and W. Ribarsky, NewsLab: Exploratory Broadcast News Video Analysis, IEEE VAST, vol.37, pp.123-130, 2007.

M. Ghoniem, M. Cornil, and B. Broeksema, Weighted maps: treemap visualization of geolocated quantitative data, IS&T/SPIE Electronic Imaging, pages 93970G-93970G. International Society for Optics and Photonics, p.91, 2015.

G. Govaert, Simultaneous clustering of rows and columns, Control and Cybernetics, vol.24, p.22, 1995.

G. Govaert, Data analysis. ISTE & Wiley, series editor édition, p.21, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00447855

G. Govaert and M. Nadif, Co-Clustering. ISTE & Wiley, series editor édition, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00933301

D. Greene and P. Cunningham, Spectral co-clustering for dynamic bipartite graphs, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), p.147, 2010.

B. Gretarsson, J. O'donovan, S. Bostandjiev, T. Höllerer, A. Asuncion et al., TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling, ACM Trans. Intell. Syst. Technol, vol.3, issue.2, p.26, 2012.

T. L. Griffiths, M. I. Jordan, J. B. Tenenbaum, and D. M. Blei, Hierarchical Topic Models and the Nested Chinese Restaurant Process, Advances in Neural Information Processing Systems, vol.16, pp.17-24, 2004.

M. Halvey and M. Keane, An assessment of tag presentation techniques, Proceedings of the 16th international conference on World Wide Web, vol.34, pp.1313-1314, 2007.

J. Han, J. Pei, Y. Yin, and R. Mao, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, vol.8, issue.1, pp.53-87, 2004.

B. Hanczar and M. Nadif, Using the bagging approach for biclustering of gene expression data, vol.74, p.69, 2011.

, Z.S. Harris. Distributional structure. Word, vol.10, issue.2-3, pp.146-162, 1954.

S. Havre, ThemeRiver: visualizing thematic changes in large document collections, IEEE TVCG, vol.8, issue.1, pp.9-20, 2002.

R. M. Heiberger and N. B. Robbins, Design of Diverging Stacked Bar Charts for Likert Scales and Other Applications, Journal of Statistical Software, vol.57, issue.5, pp.1-32, 2014.

J. Heinrich, R. Seifert, M. Burch, and D. Weiskopf, BiCluster Viewer: A Visualization Tool for Analyzing Gene Expression Data, Advances in Visual Computing, number 6938 de Lecture Notes in Computer Science, pp.641-652, 2011.

T. Hofmann, Probabilistic Latent Semantic Indexing, Proc. of the 22Nd Annual International ACM SIGIR, SIGIR '99, p.16, 1999.

D. Horta and R. J. Campello, Similarity Measures for Comparing Biclusterings, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.11, issue.5, pp.942-954, 2014.

A. Hotho, A. Nürnberger, and G. Paaß, A brief survey of text mining, Ldv Forum, vol.20, pp.19-62, 2005.

A. Huang, Similarity measures for text document clustering, Proceedings of the sixth new zealand computer science research student conference (NZC-SRSC2008), vol.19, pp.49-56, 2008.

T. Isenberg, P. Isenberg, J. Chen, M. Sedlmair, and T. Moller, A Systematic Review on the Practice of Evaluating Visualization, IEEE Transactions on Visualization and Computer Graphics, vol.19, issue.12, pp.2818-2827, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00846775

E. Kandogan, D. Soroker, S. Rohall, P. Bak, F. Van-ham et al., A reference web architecture and patterns for realtime visual analytics on large streaming data, Proc. SPIE, VDA, vol.901708, 2013.

L. Kaufman and P. Rousseeuw, Clustering by means of medoids, p.19, 1987.

L. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis, vol.344, p.19, 2009.

D. A. Keim, F. Mansmann, and J. Thomas, Visual Analytics: How Much Visualization and How Much Analytics? SIGKDD Explor. Newsl, vol.11, pp.5-8, 2010.

K. Kim, S. Ko, N. Elmqvist, and D. S. Ebert, WordBridge: Using Composite Tag Clouds in Node-Link Diagrams for Visualizing Content and Relations in Text Corpora, 44th Hawaii International Conference on System Sciences (HICSS), p.44, 2011.

M. Krstaji´ckrstaji´c, M. Najm-araghi, F. Mansmann, and D. A. Keim, Story Tracker: Incremental visual text analytics of news story development. Information Visualization, vol.12, pp.308-323, 2013.

K. Kucher and A. Kerren, Text visualization techniques: Taxonomy, visual survey, and community insights, 2015 IEEE Pacific Visualization Symposium (PacificVis), vol.32, pp.117-121, 2015.

M. L. Hunter, N. Hanson, S. Rana, L. Sengers, D. Sullivan et al., Story-Based Inquiry: A manual for investigative journalists, vol.63, p.50, 2011.

L. Labiod and M. Nadif, Co-clustering for binary and categorical data with maximum modularity, IEEE 11th International Conference on, p.22, 2011.

D. Daniel, H. Lee, and . Sebastian-seung, Learning the parts of objects by nonnegative matrix factorization, Nature, vol.401, issue.6755, p.84, 1999.

N. H. Bongshin-lee, A. K. Riche, S. Karlson, and . Carpendale, SparkClouds: Visualizing Trends in Tag Clouds, IEEE TVCG, vol.16, issue.6, p.35, 2010.

H. Lee, J. Kihm, J. Choo, J. Stasko, and H. Park, iVisClustering: An Interactive Visual Document Clustering via Topic Modeling, Computer Graphics Forum, vol.31, issue.3pt3, pp.1155-1164, 2012.

T. Leighton and S. Rao, An approximate max-flow min-cut theorem for uniform multicommodity flow problems with applications to approximation algorithms, Foundations of Computer Science, vol.20, pp.422-431, 1988.

S. Liu, M. X. Zhou, S. Pan, W. Qian, W. Cai et al., Interactive, Topicbased Visual Text Summarization and Analysis, Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM '09, p.39, 2009.

Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, Understanding of Internal Clustering Validation Measures, 2010 IEEE 10th International Conference on Data Mining (ICDM), vol.29, pp.911-916, 2010.

S. Liu, X. Wang, J. Chen, J. Zhu, and B. Guo, TopicPanorama: A full picture of relevant topics, 2014 IEEE Conference on Visual Analytics Science and Technology (VAST), vol.45, pp.183-192, 2014.

D. Luo, J. Yang, M. Krstajic, W. Ribarsky, and D. Keim, EventRiver: Visually Exploring Text Collections with Temporal References, IEEE Transactions on Visualization and Computer Graphics, vol.18, issue.1, pp.93-105, 2012.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol.1, p.19, 1967.

]. S. Madeira and A. L. Oliveira, Biclustering Algorithms for Biological Data Analysis: A Survey, IEEE/ACM Trans. Comput. Biol. Bioinformatics, vol.1, issue.1, p.147, 2004.

C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard et al., Association for Computational Linguistics (ACL) System Demonstrations, pp.55-60, 2014.

A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden et al., Twitinfo: Aggregating and Visualizing Microblogs for Event Exploration, 2011.

, Proc. CHI'11, vol.130, pp.227-236, 2011.

A. K. Mccallum, MALLET: A Machine Learning for Language Toolkit, vol.76, p.68, 2002.

, McLachlan and D. Peel. Finite mixture models, p.21, 2004.

N. Médoc and M. Stefas, Mohammad Ghoniem and Mohamed Nadif. Visual Analytics of Text Streams through Multiple Dynamic Frequency Matrices, IEEE VAST 2014, 2014.

D. Mimno, H. M. Wallach, E. Talley, M. Leenders, and A. Mccallum, Optimizing semantic coherence in topic models, Proceedings of the Conference on Empirical Methods in Natural Language Processing, vol.30, pp.262-272, 2011.

T. Munzner, A Nested Model for Visualization Design and Validation, IEEE TVCG, vol.15, issue.6, pp.921-928, 2009.

F. Murtagh and P. Contreras, Algorithms for hierarchical clustering: an overview, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.2, issue.1, p.19, 2012.

R. Navigli, A Quick Tour of Word Sense Disambiguation, Induction and Related Approaches, SOFSEM 2012: Theory and Practice of Computer Science, number 7147 de Lecture Notes in Computer Science, pp.115-129, 2012.

M. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical review E, vol.69, issue.2, p.26113, 2004.

D. Newman, Y. Noh, E. Talley, S. Karimi, and T. Baldwin, Evaluating Topic Models for Digital Libraries, Proceedings of the 10th Annual Joint Conference on Digital Libraries, JCDL '10, p.29, 2010.

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, NIPS, vol.14, pp.849-856, 2001.

[. O'connor, ;. B. O'connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith, From tweets to polls: Linking text sentiment to public opinion time series, ICWSM, vol.11, issue.122-129, pp.1-2, 2010.

P. Paatero and U. Tapper, Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, vol.5, issue.2, p.16, 1994.

A. Pérez-suárez, J. Martínez-trinidad, J. A. Carrasco-ochoa, and J. E. Medina-pagola, An algorithm based on density and compactness for dynamic overlapping clustering, Pattern Recognition, vol.46, issue.11, p.147, 2013.

A. Preli´cpreli´c, S. Bleuler, P. Zimmermann, A. Wille, P. Bühlmann et al., A systematic comparison and evaluation of biclustering methods for gene expression data, Bioinformatics, vol.22, issue.9, pp.1122-1129, 2006.

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.66, issue.336, p.23, 1971.

A. Rivadeneira, D. Gruen, M. Muller, and D. Millen, Getting our head in the clouds: toward evaluation studies of tagclouds, Proceedings of the SIGCHI conference on Human factors in computing systems, pp.995-998

, ACM, p.34, 2007.

C. Rohrdantz, D. Oelke, M. Krstaji´ckrstaji´c, and F. Fischer, Real-time visualization of streaming text data: Tasks and challenges, Proc. IEEE Workshop on Interactive Visual Text Analytics for Decision Making, vol.130, 2011.

F. Role and M. Nadif, Handling the Impact of Low Frequency Events on Cooccurrence Based Measures of Word Similarity -A Case Study of Pointwise Mutual Information, p.15, 2011.

F. Role and M. Nadif, Beyond cluster labeling: Semantic interpretation of clusters contents using a graph representation, Knowledge-Based Systems, vol.56, p.21, 2014.

S. Rufiange and M. J. Mcguffin, DiffAni: Visualizing Dynamic Graphs with a Hybrid of Difference Maps and Animation, IEEE Transactions on Visualization and Computer Graphics, vol.19, issue.12, p.42, 2013.

H. B. Saber and M. Elloumi, A New Study on Biclustering Tools, Biclusters Validation and Evaluation Functions, International Journal of Computer Science & Engineering Survey, vol.6, issue.1, p.29, 2015.

D. Sacha, M. Sedlmair, L. Zhang, J. A. Lee, D. Weiskopf et al., Human-centered machine learning through interactive visualization

B. Bruges, , p.37, 2016.

G. Salton, A. Wong, and C. S. Yang, A Vector Space Model for Automatic Indexing, Commun. ACM, vol.18, issue.11, pp.613-620, 1975.

R. Santamaría, R. Therón, and L. Quintales, A visual analytics approach for understanding biclustering results from microarray data, BMC Bioinformatics, vol.9, issue.1, p.91, 2008.

S. E. Schaeffer, Computer science review, vol.1, pp.27-64, 2007.

H. Schulz, S. Hadlak, and H. Schumann, The Design Space of Implicit Hierarchy Visualization: A Survey, IEEE Transactions on Visualization and Computer Graphics, vol.17, issue.4, p.109, 2011.

H. J. Schulz, T. Nocke, M. Heitzler, and H. Schumann, A Design Space of Visualization Tasks, IEEE Transactions on Visualization and Computer Graphics, vol.19, issue.12, pp.2366-2375, 2013.

M. Shafiei and E. Milios, Model-based overlapping co-clustering, Proceeding of SIAM Conference on Data Mining, vol.156, 2006.

M. M. Shafiei and E. E. Milios, Latent Dirichlet Co-Clustering, Sixth International Conference on Data Mining, 2006. ICDM '06, p.28, 2006.

B. Shneiderman, The eyes have it: a task by data type taxonomy for information visualizations, IEEE Symposium on Visual Languages, vol.35, p.31, 1996.

J. Stasko, R. Catrambone, M. Guzdial, and K. Mcdonald, An evaluation of space-filling information visualizations for depicting hierarchical structures, International Journal of Human-Computer Studies, vol.53, issue.5, p.92, 2000.

J. Stasko, C. Görg, and Z. Liu, Jigsaw: Supporting Investigative Analysis through Interactive Visualization, Information Visualization, vol.7, issue.2, pp.118-132, 2008.

M. Steinbach, G. Karypis, and V. Kumar, A comparison of document clustering techniques, KDD workshop on Text Mining, vol.19, 2000.

A. Strehl and J. Ghosh, Cluster Ensembles:A Knowledge Reuse Framework for Combining Multiple Partitions, Machine Learning Research, vol.29, p.23, 2002.

M. Streit, S. Gratzl, M. Gillhofer, A. Mayr, A. Mitterecker et al., Furby: fuzzy force-directed bicluster visualization, BMC Bioinformatics, vol.15, issue.6, p.4, 2014.

M. Sun, C. North, and N. Ramakrishnan, A Five-Level Design Framework for Bicluster Visualizations, IEEE TVCG, vol.20, issue.12, pp.1713-1722

M. Sun, P. Mi, C. North, and N. Ramakrishnan, BiSet: Semantic Edge Bundling with Biclusters for Sensemaking, IEEE TVCG, issue.99, p.91, 2015.

J. Thomas and K. Cook, Illuminating the path: the research and development agenda for visual analytics, 2005.

A. Thudt, U. Hinrichs, and S. Carpendale, The Bohemian Bookshelf: Supporting Serendipitous Book Discoveries Through Information Visualization, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '12, vol.50, pp.1461-1470, 2012.

;. P. Edward-r-tufte, P. Turney, and . Pantel, From Frequency to Meaning: Vector Space Models of Semantics, J. Artif. Int. Res, vol.37, issue.1, pp.141-188, 2006.

L. Vendramin, R. J. Campello, and E. R. Hruschka, Relative clustering validity criteria: A comparative overview, Statistical Analysis and Data Mining, vol.3, issue.4, p.29, 2010.

F. B. Viegas, M. Wattenberg, and J. Feinberg, Participatory Visualization with Wordle, IEEE TVCG, vol.15, issue.6, pp.1137-1144, 2009.

A. ?ili´c?ili´c and B. D. Ba?i´cba?i´c, Visualization of Text Streams: A Survey, Knowledge-Based and Intelligent Information and Engineering Systems, p.31, 2010.

. Springer, , p.32, 2010.

H. M. Wallach, I. Murray, R. Salakhutdinov, and D. Mimno, Evaluation Methods for Topic Models, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.29, 2009.

P. Wang, C. Domeniconi, and K. B. Laskey, Latent dirichlet bayesian coclustering, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, vol.28, pp.522-537, 2009.

F. Wanner, A. Stoffel, D. Jäckle, B. C. Kwon, A. Weiler et al., State-of-the-Art Report of Visual Analysis for Event Detection in Text Data Streams, EuroVis -STARs. The Eurographics Association, vol.32, 2014.

M. Wattenberg and F. B. Viégas, The Word Tree, an Interactive Visual Concordance, IEEE Transactions on Visualization and Computer Graphics, vol.14, issue.6, pp.1221-1228, 2008.

F. Wei, S. Liu, Y. Song, S. Pan, M. X. Zhou et al., TIARA: a visual exploratory text analytic system, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, p.39, 2010.

, Marcos Weskamp. News Map, vol.43, 2004.

J. A. Wise, J. J. Thomas, K. Pennock, D. Lantrip, M. Pottier et al., Visualizing the non-visual: spatial analysis and interaction with information from text documents, vol.33, pp.51-58, 1995.

W. Xu, X. Liu, and Y. Gong, Document Clustering Based on Non-negative Matrix Factorization, Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR '03, p.16, 2003.

P. Xu, N. Cao, H. Qu, and J. Stasko, Interactive visual co-cluster analysis of bipartite graphs, 2016 IEEE Pacific Visualization Symposium (PacificVis), vol.56, pp.32-39, 2016.