Clustering algorithms based on volume criteria

被引：38

作者：

Krishnapuram, R ^{[1
]}

Kim, J ^{[1
]}

机构：

[1] Colorado Sch Mines, Dept Math & Comp Sci, Golden, CO 80401 USA

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2000年 / 8卷 / 02期

基金：

美国国家科学基金会;

关键词：

clustering criteria; fuzzy clustering; image segmentation; surface approximation;

D O I：

10.1109/91.842156

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Clustering algorithms such as the K-means algorithm and the fuzzy C-means algorithm are based on the minimization of the trace of the (fuzzy) within-cluster scatter matrix. In this paper, we explore the use of determinant (volume) criteria for clustering. We derive an algorithm called the minimum scatter volume (MSV) algorithm, that minimizes the scatter volume, and another algorithm called the minimum cluster volume (MCV) that minimizes the sum of the volumes of the individual clusters. The behavior of MSV is shown to be similar to thai of K-means, whereas MCV is more versatile.

引用

页码：228 / 236

页数：9

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