Hybrid Evolutionary Multiobjective Fuzzy C-Medoids Clustering of Categorical Data

In this article, we have considered the problem of fuzzy clustering of categorical data. In this regard, the well-known fuzzy C-medoids algorithm for categorical data clustering is posed as a multiobjective optimization problem where the cluster medoids are encoded in the chromosomes of a multiobjective genetic algorithm. The chromosomes are of variable lengths to permit automatic evolution of the number of clusters. The chromosomes are updated through the medoid updating process of fuzzy C-medoids clustering. The fuzzy cluster variance and cluster separation are taken as the two objectives to be optimized simultaneously. The performance of the proposed algorithm has been compared with that of different well-known categorical data clustering algorithms and demonstrated for a variety of synthetic and real-life categorical data sets.