Textual data summarization using the Self-Organized Co-Clustering model. (July 2020)

• Record Type: Journal Article
• Authors: Selosse, Margot; Jacques, Julien; Biernacki, Christophe
• Is Part Of: Pattern recognition. Volume 103(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Co-Clustering -- Document-term matrix -- Latent block model; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2020.107315
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The SOCC model is a novel approach for clustering textual data sets. It consists in co-clustering the classic document-term frequency matrix, i.e in simultaneously clustering the document and the term they are made of. The crossing of a document-cluster and a term-cluster is called block. A structure with meaningful and non-meaningful blocks is proposed. The resulting co-clustering offers highly interpretable results for the user. 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 co-clusters. Both simulated and real data sets illustrate the efficiency of this model by its ability to easily identify relevant co-clusters.