%0 Journal Article
%T Textual data summarization using the Self-Organized Co-Clustering model
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%+ MOdel for Data Analysis and Learning (MODAL)
%A Selosse, Margot
%A Jacques, Julien
%A Biernacki, Christophe
%< avec comité de lecture
%@ 0031-3203
%J Pattern Recognition
%I Elsevier
%V 103
%P 107315
%8 2020-02
%D 2020
%R 10.1016/j.patcog.2020.107315
%K coclustering
%K Latent Block Model
%K document-term matrix
%Z Mathematics [math]/Statistics [math.ST]Journal articles
%X Recently, different studies have demonstrated the use of co-clustering, a data mining technique which simultaneously produces row-clusters of observations and column-clusters of features. The present work introduces a novel co-clustering model to easily summarize textual data in a document-term format. In addition to highlighting homogeneous co-clusters as other existing algorithms do we also distinguish noisy co-clusters from significant co-clusters, which is particularly useful for sparse document-term matrices. Furthermore, our model proposes a structure among the significant co-clusters, thus providing improved interpretability to users. The approach proposed contends with state-of-the-art methods for document and term clustering and offers user-friendly results. The model relies on the Poisson distribution and on a constrained version of the Latent Block Model, which is a probabilistic approach for co-clustering. A Stochastic Expectation-Maximization algorithm is proposed to run the model’s inference as well as a model selection criterion to choose the number of coclusters. Both simulated and real data sets illustrate the eciency of this model by its ability to easily identify relevant co-clusters.
%G English
%2 https://hal.science/hal-02115294v3/document
%2 https://hal.science/hal-02115294v3/file/manuscript.pdf
%L hal-02115294
%U https://hal.science/hal-02115294
%~ CNRS
%~ INRIA
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ INRIA-LILLE
%~ INSMI
%~ ERIC
%~ INRIA_TEST
%~ LORIA2
%~ TESTALAIN1
%~ INRIA2
%~ UNIV-LILLE
%~ LYON2
%~ UDL
%~ UNIV-LYON
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%~ LPP-MATH
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