Title :
A new bi-clustering approach using topological maps
Author :
Chaibi, Amine ; Lebbah, Mustapha ; Azzag, Hanane
Author_Institution :
LIPN, Univ. of Paris 13, Villetaneuse, France
Abstract :
In this paper, we propose a new bi-clustering algorithm based on self-organizing maps titled BiTM (Bi-clustering using Topological Map). BiTM provides a simultaneous clustering of rows and columns of the data matrix in order to increase the homogeneity of bi-clusters by respecting neighborhood relationship and using a single map. BiTM maps provide a new topological visualization of the bi-clusters. Experimental results and comparison studies show that BiTM improves the results in term of bi-clustering and visualization.
Keywords :
data visualisation; pattern clustering; self-organising feature maps; BiTM maps; biclustering using topological map; column clustering; data matrix; neighborhood relationship; row clustering; self-organizing maps; topological visualization; Breast; Cancer; Glass; Heart; Indexes; Prototypes; Sonar; Bi-clustering; self-organizing maps; visualization;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6706855