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

Selosse, Margot; Jacques, Julien; Biernacki, Christophe

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1016/j.patcog.2020.107315

Title :

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

Author(s) :

Selosse, Margot [Auteur]
Entrepôts, Représentation et Ingénierie des Connaissances [ERIC]
Jacques, Julien [Auteur]
Entrepôts, Représentation et Ingénierie des Connaissances [ERIC]
Biernacki, Christophe [Auteur]

MOdel for Data Analysis and Learning [MODAL]

Journal title :

Pattern Recognition

Pages :

107315

Publisher :

Elsevier

Publication date :

2020-02

ISSN :

0031-3203

English keyword(s) :

coclustering
Latent Block Model
document-term matrix

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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