Textual data summarization using the Self-Organized Co-Clustering model - Archive ouverte HAL
Journal Articles Pattern Recognition Year : 2020

Textual data summarization using the Self-Organized Co-Clustering model

Abstract

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.
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Dates and versions

hal-02115294 , version 1 (30-04-2019)
hal-02115294 , version 2 (09-12-2019)
hal-02115294 , version 3 (24-02-2020)

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Margot Selosse, Julien Jacques, Christophe Biernacki. Textual data summarization using the Self-Organized Co-Clustering model. Pattern Recognition, 2020, 103, pp.107315. ⟨10.1016/j.patcog.2020.107315⟩. ⟨hal-02115294v3⟩
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