A Tabu Search Based Algorithm for Clustering Categorical Data Sets

Authors:
Joyce C. Wong;Michael K. Ng
Affiliations:
-;-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

Citing 3
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Tabu Search

Tabu Search
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

Data Mining and Knowledge Discovery
A fuzzy k-modes algorithm for clustering categorical data

IEEE Transactions on Fuzzy Systems

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Abstract

Clustering methods partition a set of objects into clusters such that objects in the same cluster are more similar to each other than objects in different clusters according to some defined criteria. In this paper, we present an algorithm, called tabu search fuzzy k-modes, to extend the fuzzy k-means paradigm to categorical domains. Using the tabu search based technique, our algorithm can explore the solution space beyond local optimality in order to aim at finding a global optimal solution of the fuzzy clustering problem. It is found that our algorithm performs better, in terms of accuracy, than the fuzzy k-modes algorithm.