A modified version of the K-means algorithm with a distance based on cluster symmetry
IEEE Transactions on Pattern Analysis and Machine IntelligencePublished 1 June 2001
Mu‐Chun Su, Chien-Hsing Chou
Citations389
SJR quartileQ1
SJR score3.91
SNIP5.99
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TL;DR
A modified version of the K-means algorithm is proposed that adopts a novel nonmetric distance measure based on the idea of "point symmetry" that can be applied in data clustering and human face detection.
Abstract
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.
Keywords
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