DOFL: Kernel Based Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering

Abstract

In recent years, clustering finds various applications in most of the fields like networking, telecommunications and medical domain. So, various clustering algorithms are developed by researchers to improve the clustering performance. Among this, optimisation based clustering algorithm is one of the recently developed algorithms for clustering process to discover the optimal clusters based on the objective function. Accordingly, in this paper, we have proposed directive operative fractional lion optimization using kernel based clustering algorithm. Initially, three objective functions are developed based on the kernel function. Then, the result of objective function is selected as the fitness function for the proposed optimisation algorithm called directive operative based fractional lion algorithm to select the rapid centroid of the data cluster. Finally, the performance of the proposed kernel based directive operative fractional lion algorithm (DOFL) algorithm is evaluated based on the various evaluation metrics such as cluster accuracy, jaccard coefficient and rand coefficient using the both benchmarked iris and wine data sets. From the research outcome, we can prove that, the maximum clustering accuracy of 79% is obtained by the proposed optimization clustering algorithm compared to other existing clustering algorithms such as Particle Swarm clustering algorithm (PSC), modified Particle Swarm clustering algorithm (mPSC), lion algorithm and fractional lion algorithm.