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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7-8/2013

01.06.2013 | Original Article

Bi-clustering continuous data with self-organizing map

verfasst von: Khalid Benabdeslem, Kais Allab

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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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.
Vorheriger Artikel A simple and fast representation-based face recognition method
Nächster Artikel Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method

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Metadaten
Titel
Bi-clustering continuous data with self-organizing map
verfasst von
Khalid Benabdeslem
Kais Allab
Publikationsdatum
01.06.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1047-6

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