Development of Fractional Genetic PSO Algorithm for Multi Objective Data Clustering

Abstract

Clustering is the task of finding natural partitioning within a data set such that data items within the same group are more similar than those within different groups. The performance of the traditional K-Means and Bisecting K-Means algorithm degrades as the dimensionality of the data increases. In order to find better clustering results, it is important to enhance the traditional algorithms by incorporating various constraints. Hence it is planned to develop a Multi-Objective Optimization (MOO) technique by including different objectives, like MSE, Stability measure, DB index, XB-index and sym-index. These five objectives will be used as fitness function for the proposed Fractional Genetic PSO algorithm (FGPSO) which is the hybrid optimization algorithm to do the clustering process. The performance of the proposed multi objective FGPSO algorithm will be evaluated based on clustering accuracy. Finally, the applicability of the proposed algorithm will be checked for some benchmark data sets available in the UCI machine learning repository.

Evolutionary computing based hybrid bisecting clustering algorithm for multidimensional data

“…The CGABC algorithm has shown improvement over other baseline experiments conducted such as HBK-Means [20], CHBK-Means [22], FGPSO [26]. This is illustrated in table 2.…”