Clustering feature vectors with mixed numerical and categorical attributes

被引：6

作者：

Brouwer, Roelof K.

机构：

[1] Department of Mechanical and Mechatronics Engineering, Stellenbosch University

来源：

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2008年 / 1卷 / 04期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Fuzzy clustering; gradient descent; categorical; nominal clustering; fuzzy c-means;

D O I：

10.1080/18756891.2008.9727625

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes a method for finding a fuzzy membership matrix in case of numerical and categorical features. The set of feature vectors with mixed features is mapped to a set of feature vectors with only real valued components with the condition that the new set of vectors has the same proximity matrix as the original feature vectors. This new set of vectors is then clustered using fuzzy c-means. Simulations show the method to be very effective in comparison with other methods.

引用

页码：285 / 298

页数：14

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