Robust centroids using fuzzy clustering with feature partitions

被引：8

作者：

Alexiuk, MD ^{[1
]}

Pizzi, NJ ^{[1
]}

机构：

[1] Natl Res Council Canada, Inst Biodiagnost, Winnipeg, MB R3B 1Y6, Canada

来源：

PATTERN RECOGNITION LETTERS | 2005年 / 26卷 / 08期

关键词：

functional magnetic resonance imaging; fuzzy c-means; noise;

D O I：

10.1016/j.patrec.2004.09.055

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fuzzy c-means with feature partitions uses a generalized metric on feature subsets to increase centroid robustness. Each feature partition may use a unique metric and is weighted for relevance. This method is demonstrated on synthetic and real datasets. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：1039 / 1046

页数：8

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