ComputingComputer ScienceData MiningData StructuresMachine LearningDocument ClusteringText ClusteringCluster SimilarityHierarchical Cluster AnalysisHierarchical ClusteringSelf-organizing MapsSimilarity Measure Selection
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. 1
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