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Bi-clustering continuous data with self-organizing map

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Abstract

In this paper, we present a new SOM-based bi-clustering approach for continuous data. This approach is called Bi-SOM (for Bi-clustering based on Self-Organizing Map). The main goal of bi-clustering aims to simultaneously group the rows and columns of a given data matrix. In addition, we propose in this work to deal with some issues related to this task: (1) the topological visualization of bi-clusters with respect to their neighborhood relation, (2) the optimization of these bi-clusters in macro-blocks and (3) the dimensionality reduction by eliminating noise blocks, iteratively. Finally, experiments are given over several data sets for validating our approach in comparison with other bi-clustering methods.

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Authors and Affiliations

  1. University of Lyon 1, LIRIS, CNRS-UMR 5205, 69622, Lyon, France

    Khalid Benabdeslem

  2. University of Lyon 1, GAMA, 69622, Lyon, France

    Kais Allab

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  1. Khalid Benabdeslem
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  2. Kais Allab
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Correspondence to Khalid Benabdeslem.

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Benabdeslem, K., Allab, K. Bi-clustering continuous data with self-organizing map. Neural Comput & Applic 22, 1551–1562 (2013). https://doi.org/10.1007/s00521-012-1047-6

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