Improved Fuzzy Clustering Techniques for Categorical Data

Clustering is a widely used technique in data mining application for discovering patterns in underlying data. Most traditional clustering algorithms are limited in handling datasets that contain categorical attributes. Howerver, datasets with categorical types of attributes are common in real life data mining problem. For these data sets, no inherent distance measure, like the Euclidean distance, would work to compute the distance between two catgorical objects. In this article, we have described differential evolution, genetic algorithm and simulated annealing based fuzzy clustering. The performance of the proposed algorithms have been compared with that of different well known categorical data clustering algorithms and demonstrated for a variety of artificial and real life categorical data sets. Statistical significance test has been performed to establish the superiority of the proposed algorithms.