Model Based Co-clustering of Mixed Numerical and Binary Data

• URL: http://arxiv.org/abs/2212.11725v1
• Date: Thu, 22 Dec 2022 14:16:08 GMT
• Title: Model Based Co-clustering of Mixed Numerical and Binary Data
• Authors: Aichetou Bouchareb (SAMM), Marc Boull\'e, Fabrice Cl\'erot, Fabrice Rossi (CEREMADE)
• Abstract summary: Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. In this article, we extend the latent block models based co-clustering to the case of mixed data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. Many approaches have been studied and have shown their capacity to extract such structures in continuous, binary or contingency tables. However, very little work has been done to perform co-clustering on mixed type data. In this article, we extend the latent block models based co-clustering to the case of mixed data (continuous and binary variables). We then evaluate the effectiveness of the proposed approach on simulated data and we discuss its advantages and potential limits.

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