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Clustering by competitive agglomeration
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DOI：10.1016/S0031-3203(96)00140-9.png)
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Abstract
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We present a new clustering algorithm called Competitive Agglomeration (CA), which minimizes an objective function that incorporates the advantages of both hierarchical and partitional clustering. The CA algorithm produces a sequence of partitions with a decreasing number of clusters. The initial partition has an over specified number of clusters, and the final one has the ''optimal'' number of clusters. The update equation in the CA algorithm creates an environment in which clusters compete for feature points and only clusters with large cardinalities survive. The algorithm can incorporate different distance measures in the objective function to find an unknown number of clusters of various shapes. (C) 1997 Pattern Recognition Society.
Keywords:
unsupervised clustering
fuzzy clustering
competitive agglomeration
cluster validity
line detection
curve detection
plane fitting
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