Localized feature selection for clustering

被引：53

作者：

Li, Yuanhong ^{[1
]}

Dong, Ming ^{[1
]}

Hua, Jing ^{[1
]}

机构：

[1] Wayne State Univ, Dept Comp Sci, Detroit, MI 48202 USA

来源：

PATTERN RECOGNITION LETTERS | 2008年 / 29卷 / 01期

基金：

美国国家科学基金会;

关键词：

clustering; unsupervised learning; feature selection; scatter separability;

D O I：

10.1016/j.patrec.2007.08.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for individual clusters. A sequential backward search is then applied to find the optimal (maybe local) feature subsets for each cluster. Our experimental results show the need for feature selection in clustering and the benefits of selecting features locally. (C) 2007 Elsevier B.V. All rights reserved.

引用

页码：10 / 18

页数：9

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