An PSO-SFLA Based Ensemble Link Weighted Triple Quality Algorithm to Improve the Performance of Clustering over Categorical Data Clustering

Abstract

This paper focus on solving the issues related to the occurrence of irrelevant and null information during cluster partitioning. Hence, to avoid the serious issue arising due to such improper dataset, the proposed method uses a link based cluster ensemble technique uses weighted triple quality and multi-view point using entropy and similarity measurement, respectively. It ensembles the objects into clusters by suitably eliminating the local optimal problem and the quality of clustering is improved by reducing the high dimensional datasets. The clustering is performed using hybrid particle Swarm Optimization (PSO) - Shuffled Frog Leaping Algorithm (SFLA) algorithm. The proposed method is evaluated on categorical datasets to test its effectiveness in terms of Clustering Accuracy (CA), Normalized Mutual Information (NMI) and Adjusted Rank Indices (ARI). The results shows that the proposed approach attains better finalized clusters than the other conventional methods.

Keywords: Bipartite Spectral Algorithm, Entropy Weighted Triple Quality, PSO-SFLA, Multiview point similarity measure.