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A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
June 2001 (vol. 23 no. 6)
pp. 674-680

Abstract—In this paper, we propose a modified version of the K-means algorithm to cluster data. The proposed algorithm adopts a novel nonmetric distance measure based on the idea of “point symmetry.” This kind of “point symmetry distance” can be applied in data clustering and human face detection. Several data sets are used to illustrate its effectiveness.

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Index Terms:
Data clustering, pattern recognition, k-means algorithm, face detection.
Citation:
Mu-Chun Su, Chien-Hsing Chou, "A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 674-680, June 2001, doi:10.1109/34.927466
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