Title :
A modified version of the K-means algorithm with a distance based on cluster symmetry
Author :
Su, Mu-Chun ; Chou, Chien-Hsing
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-Li, Taiwan
fDate :
6/1/2001 12:00:00 AM
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 :
computer vision; data handling; face recognition; pattern clustering; K-means algorithm; cluster symmetry; data clustering; human face recognition; nonmetric distance measure; pattern recognition; point symmetry distance; Algorithm design and analysis; Clustering algorithms; Covariance matrix; Engineering in medicine and biology; Euclidean distance; Face detection; Humans; Image analysis; Pattern analysis; Pattern recognition;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
