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A Maximum Variance Cluster Algorithm
September 2002 (vol. 24 no. 9)
pp. 1273-1280

Abstract—We present a partitional cluster algorithm that minimizes the sum-of-squared-error criterion while imposing a hard constraint on the cluster variance. Conceptually, hypothesized clusters act in parallel and cooperate with their neighboring clusters in order to minimize the criterion and to satisfy the variance constraint. In order to enable the demarcation of the cluster neighborhood without crucial parameters, we introduce the notion of foreign cluster samples. Finally, we demonstrate a new method for cluster tendency assessment based on varying the variance constraint parameter

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Index Terms:
Cluster analysis, partitional clustering, cluster tendency assessment, cluster validity.
Citation:
Cor J. Veenman, Marcel J.T. Reinders, Eric Backer, "A Maximum Variance Cluster Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1273-1280, Sept. 2002, doi:10.1109/TPAMI.2002.1033218
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