Clustering by multi objective genetic algorithm

The aim of the paper is to study a real coded multi objective genetic algorithm based K-clustering, where K represents the number of clusters, may be known or unknown. If the value of K is known, it is called K-clustering algorithm. The searching power of Genetic Algorithm (GA) is exploited to get for proper clusters and centers of clusters in the feature space to optimize simultaneously intra-cluster distance (Homogeneity) (H) and inter-cluster distances (Separation) (S). Maximization of 1/H and S are the twin objectives of Multi Objective Genetic Algorithm (MOGA) achieved by measuring H and S using Euclidean distance metric, suitable for continuous features (attributes). We have selected 10 data sets from the UCI machine learning repository containing continuous features only to validate the proposed algorithms. All-important steps of algorithms are shown here. At the end, classification accuracies obtained by best chromosomes are shown.