Clustering of Self-Organizing Map

Abstract. In this paper, we present a new similarity measure for a clustering self-organizing map which will be reached using a new approach of hierarchical clustering. (1) The similarity measure is composed from two terms: weighted Ward distance and Euclidean distance weighted by neighbourhood function. (2) An algorithm inspired from artificial ants named AntTree will be used to cluster a self-organizing map. This algorithm has the advantage to provide a hierarchy of referents with a low complexity (near the n log(n)). The SOM clustering including the new measure is validated on several public data bases.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Hanane Azzag, Mustapha Lebbah
• Keywords: Clustering Self-organizing Map, ESANN 2008, Neural Networks, Self-Organizing Map, Similarity Measure