A fuzzy k-modes algorithm for clustering categorical data

被引：283
|
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
收藏
页码：446 / 452
页数：7
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