@article { author = {Ghayekhloo, M. and Menhaj, M. B. and Azimi, R. and Shekari, E.}, title = {NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map}, journal = {AUT Journal of Modeling and Simulation}, volume = {49}, number = {2}, pages = {133-142}, year = {2017}, publisher = {Amirkabir University of Technology}, issn = {2588-2953}, eissn = {2588-2961}, doi = {10.22060/miscj.2016.850}, 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.}, keywords = {clustering,game theory,self-organizing map,vector quantization}, url = {https://miscj.aut.ac.ir/article_850.html}, eprint = {https://miscj.aut.ac.ir/article_850_f0ab9b6e8ac07d0e8ea61b493ea2e467.pdf} }