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

Margot Selosse (1) , Julien Jacques (1) , Christophe Biernacki (2)

1 ERIC - Entrepôts, Représentation et Ingénierie des Connaissances
2 MODAL - MOdel for Data Analysis and Learning

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.

Mots clés: coclustering; Latent Block Model; document-term matrix

Domaines: Statistiques [math.ST]

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⟩