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12

ESANN
2008

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Clustering of Self-Organizing Map

13 years 5 months ago

Clustering of Self-Organizing Map

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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.

Hanane Azzag, Mustapha Lebbah

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	ESANN
Authors	Hanane Azzag, Mustapha Lebbah

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