Clustering Algorithms: a Review
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A review of clustering concepts and algorithms is provided emphasizing: (a) output cluster structure, (b) input data kind, and (c) criterion.
A dozen cluster structures is considered including those used in either supervised or unsupervised learning or both.
The techniques discussed cover such algorithms as nearest neighbor, K-Means (moving centers), agglomerative clustering, conceptual clustering, EM-algorithm, high-density clustering, and back-propagation.
Interpretation is considered as achieving clustering goals (partly, via presentation of the same data with both extensional and intensional forms of cluster structures).
KeywordsCluster Technique Cluster Structure Classification Structure Local Search Algorithm Average Similarity
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