Improved ISODATA Clustering Method with Parameter Estimation based on Genetic Algorithm

ISODATA clustering method with merge and split parameters as well as initial cluster center determination with GA: Genetic Algorithm is proposed. Although ISODATA method is well-known clustering method, there is a problem that the iteration and clustering result is strongly depending on the initial parameters, especially the threshold for merge and split. Furthermore, it shows a relatively poor clustering performance in the case that the probability density function of data in concern cannot be expressed with convex function. To overcome this situation, GA is introduced for the determination of initial cluster center as well as the threshold of merge and split between constructing clusters. Through experiments with simulated data, the well-known the University of California, Irvine: UCI repository data for clustering performance evaluations and ASTER/VNIR: Advanced Visible and Near Infrared Radiometer onboard Terra satellite of imagery data, the proposed method is confirmed to be superior to the conventional ISODATA method.