Clustering with competing self-organizing maps

Competing self-organizing maps are used to cluster data. Because maps are more complicated than single stereotypes, this clustering is different from k-means clustering in that the proper number of clusters will be discovered. This discovery process for the number of clusters is studied and compared to k-means clustering. Also, because self-organizing maps are probabilistic algorithms, the frequency of a clustering outcome is used as a measure of the validity of the clustering.<
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