A variable-selection heuristic for K-means clustering

被引：98

作者：

Brusco, MJ ^{[1
]}

Cradit, JD ^{[1
]}

机构：

[1] Florida State Univ, Coll Business, Dept Mkt, Tallahassee, FL 32306 USA

来源：

PSYCHOMETRIKA | 2001年 / 66卷 / 02期

关键词：

cluster analysis; K-means partitioning; variable selection; heuristics;

D O I：

10.1007/BF02294838

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

One of the most vexing problems in cluster analysis is the selection and/or weighting of variables in order to include those that truly define cluster structure, while eliminating those that might mask such structure. This paper presents a variable-selection heuristic For nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. The heuristic was subjected to Monte Carlo testing across more than 2200 datasets with known cluster structure. The results indicate the heuristic is extremely effective at eliminating masking variables. A cluster analysis of real-world financial services data revealed that using the variable-selection heuristic prior to the K-means algorithm resulted in greater cluster stability.

引用

页码：249 / 270

页数：22

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