Evaluation of Frequent Pattern Growth Based Fuzzy Particle Swarm Optimization Approach for Web Document Clustering

Abstract

Soft and hard clustering efficiency evaluation of novel approach of frequent pattern growth based fuzzy particle swarm optimization for clustering web documents is studied and analyzed in this paper. The conventional approaches K-Means and Fuzzy c-means (FCM) fails with regard to random initialization and local minima hookups. To overcome this drawbacks, bio inspired mechanisms like genetic algorithm, ant colony optimization and particle swarm optimization (PSO) are used to optimize the K-means and FCM clustering. The major contribution of the novel method are three fold. Primarily in its ways to automatically find effective cluster numbers, cluster centroids and swarms for the bio inspired fuzzy particle swarm optimization. Second in yielding fuzzy overlapping clusters using the FCM objective function overcoming the drawbacks of the existing methods. Third, the methodology discusses in this paper prunes out the irrelevant elements from the search space and thereby retains all relationships with search query as semantic conditionally relatable sets. The evaluation results show that our proposed approach performs better for Adjusted Rand Index (ARI), Normalized Mutual Information (NMI) and Adjusted Concordance Index (ACI) against various distance based similarity measures and FCMPSO.