A fuzzy k-modes algorithm for clustering categorical data

被引：289

作者：

Huang, ZX ^{[1
]}

Ng, MK

机构：

[1] Management Informat Principles Ltd, Melbourne, Vic, Australia

[2] Univ Hong Kong, Dept Math, Hong Kong, Peoples R China

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 1999年 / 7卷 / 04期

关键词：

categorical data; clustering; data mining; fuzzy partitioning; k -means algorithm;

D O I：

10.1109/91.784206

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.

引用

页码：446 / 452

页数：7

共 18 条

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