DocumentCode
820549
Title
A maximum variance cluster algorithm
Author
Veenman, Cor J. ; Reinders, Marcel J T ; Backer, Eric
Author_Institution
Dept. of Mediamatics, Delft Univ. of Technol., Netherlands
Volume
24
Issue
9
fYear
2002
fDate
9/1/2002 12:00:00 AM
Firstpage
1273
Lastpage
1280
Abstract
We present a partitional cluster algorithm that minimizes the sum-of-squared-error criterion while imposing a hard constraint on the cluster variance. Conceptually, hypothesized clusters act in parallel and cooperate with their neighboring clusters in order to minimize the criterion and to satisfy the variance constraint. In order to enable the demarcation of the cluster neighborhood without crucial parameters, we introduce the notion of foreign cluster samples. Finally, we demonstrate a new method for cluster tendency assessment based on varying the variance constraint parameter
Keywords
minimisation; pattern clustering; statistical analysis; cluster neighborhood demarcation; cluster tendency assessment; foreign cluster samples; maximum variance cluster algorithm; partitional cluster algorithm; sum-of-squared-error criterion minimization; Clustering algorithms;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2002.1033218
Filename
1033218
Link To Document