Bi-clustering continuous data with self-organizing map

Khalid Benabdeslem (1) , Kais Allab (2)

1 DM2L - Data Mining and Machine Learning
2 GAMA - Graphes, Apprentissage et Multi-Agents

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.

Mots clés: Dimensionality reduction; SOM; Bi-clustering

Domaines: Machine Learning [stat.ML]; Réseau de neurones [cs.NE]

Khalid Benabdeslem, Kais Allab. Bi-clustering continuous data with self-organizing map. Neural Computing and Applications, 2012, 22 (7-8), pp.1551-1562. ⟨10.1007/s00521-012-1047-6⟩. ⟨hal-00874676⟩