A new swarm-based efficient data clustering approach using KHM and fuzzy logic

Clustering is a useful technique to create different groups of objects on the basis of their nature. Objects of same group are of similar in nature and differ to the objects of other groups. Clustering has proved its importance in various fields such as information retrieval, bioinformatics, image processing and many others. In this paper, particle swarm optimization (PSO) technique is used with K-harmonic means (KHM) for clustering. PSO overcomes the limitations of KHM like local optimum problem. Fuzzy logic is also employed in this paper to make PSO adaptive in nature by controlling various parameters. The performance of the proposed approach is validated on five benchmark datasets in terms of inter-clustering distance, intra-clustering distance, F-measure and fitness value. The results of proposed approach are compared with well-known conventional clustering techniques such as K-means, KHM and fuzzy C-means along with different state-of-the-art clustering approaches. Two text-based benchmark datasets such as CACM and CISI are also used to test the performance of all clustering approaches. The proposed clustering approach gives better results in comparison with other clustering approaches as clear from both the experimental and statistical analyses.