ISODATA clustering with parameter (threshold for merge and split) estimation based on GA: Genetic Algorithm

A method of GA: Genetic Algorithm based ISODATA clustering is proposed. GA clustering is now widely available. One of the problems for GA clustering is a poor clustering performance due to the assumption that clusters are represented as convex functions. Well known ISODATA clustering has parameters of threshold for merge and split. The parameters have to be determined without any assumption (convex functions). In order to determine the parameters, GA is utilized. Through comparatives studies between with and without parameter estimation with GA utilizing well known UCI Repository data clustering performance evaluation, it is found that the proposed method is superior to the original ISODATA. It is found that the experimental results show that clustering error of the proposed method is 2 to 10 times much smaller than that of the existing method. It is also found that the elite selection strategy is superior to the average selection through experiments.