Multiobjective Genetic Fuzzy Clustering of Categorical Attributes
Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandyopadhyay · 2007
Most of the algorithms designed for categorical data clustering optimize a single measure of the clustering goodness. Such a single measure may not be appropriate for different kinds of data sets. Therefore, consideration of multiple, often conflicting, objectives appears to be natural for this problem. In this article a multiobjective genetic algorithm based approach for fuzzy clustering of categorical data is proposed. The performance of the proposed technique has been compared with that of the other well known categorical data clustering algorithms. For this purpose, various synthetic and real life categorical data sets have been considered. Statistical significance test has been conducted to establish the significant superiority of the proposed multiobjective approach.