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
fDate :
9/1/2002 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2002.1033218
