A modified version of the K-means algorithm with a distance basedon cluster symmetry
Mu-Chun Su
Chien-Hsing Chou
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-Li;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 2001
Volume: 23,
Issue: 6
On page(s): 674-680
ISSN: 0162-8828
References Cited: 24
CODEN: ITPIDJ
INSPEC Accession Number: 6967923
Digital Object Identifier: 10.1109/34.927466
Current Version Published: 2002-08-07
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
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
