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Communication Dans Un Congrès Année : 2016

Stochastic Co-clustering for Document-Term Data

Aghiles Salah
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Mohamed Nadif

Résumé

Co-clustering is more useful than one-sided clustering when dealing with high dimensional sparse data. We propose to address the aim of document clustering with a generative model-based co-clustering approach. To this end, we rely on a particular mixture of von Mises-Fisher distributions and propose a new parsimonious model allowing to reveal a block diagonal structure as well as a good partitioning of documents and terms. Then, by setting the estimate of the model parameters under the maximum likelihood (ML) approach, we derive three novel co-clustering algorithms: a soft one and two stochastic variants. Empirical results on numerous simulated and real-world datasets, demonstrate the advantages of our approach to model and co-cluster high dimensional sparse data.

Dates et versions

hal-01359522 , version 1 (02-09-2016)

Identifiants

Citer

Aghiles Salah, Nicoleta Rogovschi, Mohamed Nadif. Stochastic Co-clustering for Document-Term Data. Proceedings of the 2016 SIAM International Conference on Data Mining, May 2016, MIAMI, United States. pp.306-314, ⟨10.1137/1.9781611974348.35⟩. ⟨hal-01359522⟩
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