A Mutation factor based Clonal Selection Algorithm for Data Clustering

The Clonal Selection hypothesis is a widely accepted model for the immune system's response to infection in human body. Clonal Selection Algorithms (CSA) is a special class of Immune algorithms (IA), inspired by the Clonal Selection Principle. To improve the Algorithm's ability to perform better, this CSA has been modified by implementing two new concepts called Fixed Mutation Factor and Ladder Mutation Factor. Fixed Mutation Factor maintains a constant Factor throughout the process, where as Ladder Mutation Factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches are tested on twelve datasets. The proposed method applied on the data clustering, which is an important task of data mining. Experimental results empirically shows that the proposed Ladder Mutation based Clonal Selection Algorithm (LMCSA) and Fixed Mutation Clonal selection Algorithm (FMCSA) significantly out performs the existing CLONALG method in terms of quality of the solution.