A fuzzy k-modes algorithm for clustering categorical data

Zhexue Huang; Michael K. Ng

doi:10.1109/91.784206

A fuzzy k-modes algorithm for clustering categorical data

Zhexue Huang^*, Michael K. Ng

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › peer-review

443 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	446-452
Number of pages	7
Journal	IEEE Transactions on Fuzzy Systems
Volume	7
Issue number	4
DOIs	https://doi.org/10.1109/91.784206
Publication status	Published - Aug 1999

User-Defined Keywords

Categorical data
Clustering
Data mining
Fuzzy partitioning
k-means algorithm

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10.1109/91.784206

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abstract = "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.",

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A fuzzy k-modes algorithm for clustering categorical data

Abstract

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