A Comparative Study on Clustering Algorithms

In this paper, we give a comparison of four methods for solving clustering problems, including similarity-based fuzzy clustering (SFC), elliptic basis function (EBF), versatile elliptic basis function (VEBF), and similarity-based fuzzy clustering with principal component analysis (PCSFC). PCSFC is a modified version of SFC with rotation, while VEBF is a refined version of EBF. SFC and PCSFC are based on Gaussian functions, and EBF and VEBF are based on elliptic basis functions. Each method is briefly described, together with the pros and cons of the solution it provides. Simulation results are presented to compare the induced errors between true values and predicted values obtained from using different methods to do clustering for benchmark data sets.