An entropy clustering analysis based on genetic algorithm

Abstract

Clustering analysis quantifies similarities (dissimilarities) between objects in a given dataset and discovers the hidden characteristics of each cluster. However, researchers often have difficulty in setting optimal parameters for clustering analysis when they attempt to obtain the optimal clustering. This work presents an entropy-based efficient clustering technique utilizing principles of genetic algorithm (GA), unlike previous clustering method [24] which employs parameter setting. The proposed method considers the data spread to determine the adaptive threshold within parameters optimized by genetic algorithm. The fitness function of genetic algorithm is defined as clustering accuracy. Four datasets in the UCI database are selected as the experimental data to compare the accuracy of the proposed algorithm with the three clustering methods. Results of this study demonstrate that the proposed algorithm outperforms listing methods.