Abstract
Classical clustering methods, such as partitioning and hierarchical clustering algorithms, often fail to deliver satisfactory results, given clusters of arbitrary shapes. Motivated by a clustering validity index based on inter-cluster and intra-cluster density, we propose that the clustering validity index be used not only globally to find optimal partitions of input data, but also locally to determine which two neighboring clusters are to be merged in a hierarchical clustering of Self-Organizing Map (SOM). A new two-level SOM-based clustering algorithm using the clustering validity index is also proposed. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data in a better way than classical clustering algorithms on an SOM.
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Wu, S., Chow, T.W.S. Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index. Neural Processing Letters 17, 253–271 (2003). https://doi.org/10.1023/A:1026083612746
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DOI: https://doi.org/10.1023/A:1026083612746