NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

Abstract

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different strategies of Game Theory are proposed to provide a competitive game for nonwinningneurons to participate in the learning phase and obtain more input patterns. The performanceof the proposed clustering analysis is evaluated and compared with that of the K-means, SOM andNG methods using different types of data. The clustering results of the proposed method and existingstate-of-the-art clustering methods are also compared which demonstrates a better accuracy of theproposed clustering method.