Improved Crisp and 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. However, 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 categorical objects. In this article, we have described two algorithms based on genetic algorithm and simulated annealing in the field of crisp and fuzzy domain. The performance of the proposed algorithms has been compared with that of different well known categorical data clustering algorithms in crisp and fuzzy domain and demonstrated for a variety of artificial and real life categorical data sets. Also statistical significance tests have been performed to establish the superiority of the proposed algorithms.