A Comprehensive Analysis of Kernelized Hybrid Clustering Algorithms with Firefly and Fuzzy Firefly Algorithms

Abstract

In order to handle the problem of linear separability in the early data clustering algorithms, Euclidean distance is being replaced with Kernel functions as measures of similarity. Another problem with the clustering algorithms is the selection of initial centroids randomly, which affects not only the final result but also decreases the convergence rate. Optimal selection of initial centroids through optimization algorithms like Firefly or Fuzzy Firefly algorithms provide partial solution to this problem. In this paper, we focus on two kernels; Gaussian and Hyper-tangent and use both Firefly and Fuzzy Firefly algorithms separately along with algorithms like FCM, IFCM and RFCM and analyse their efficiency using two measures DB and D. Our analysis concludes that RFCM with Hyper-tangent kernel and fuzzy firefly produce the best results with fastest convergence rate. We use the two images; MRI scan of a human brain and blood cancer cells for our analysis.