DocumentCode
1499782
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
Volume
23
Issue
6
fYear
2001
fDate
6/1/2001 12:00:00 AM
Firstpage
674
Lastpage
680
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.927466
Filename
927466
Link To Document