A maximum variance cluster algorithm

被引：361

作者：

Veenman, CJ ^{[1
]}

Reinders, MJT ^{[1
]}

Backer, E ^{[1
]}

机构：

[1] Delft Univ Technol, Fac Infomat Technol & Syst, Dept Mediamat, NL-2600 GA Delft, Netherlands

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2002年 / 24卷 / 09期

关键词：

cluster analysis; partitional clustering; cluster tendency assessment; cluster validity;

D O I：

10.1109/TPAMI.2002.1033218

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1273 / 1280

页数：8

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