IJBTT

An Efficient Nonnegative Matrix Factorization & Game Theoretic Framework Based Data Clustering

	International Journal of Computer Trends and Technology (IJCTT)
	© 2017 by IJCTT Journal
	Volume-49 Number-1
	Year of Publication : 2017
	Authors : Dr. V. Umadevi, R.Sumithra
	DOI :  10.14445/22312803/IJCTT-V49P109

Dr. V. Umadevi, R.Sumithra "An Efficient Nonnegative Matrix Factorization & Game Theoretic Framework Based Data Clustering". International Journal of Computer Trends and Technology (IJCTT) V49(1):51-57, July 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
This Mostly, factorization of matrices is not unique, Non-negative Matrix Factorization (NMF) changes from the Principal Component Analysis, Singular Value Decomposition, Nystrom Method, and it imposes the controls that the factors must be non-negative. The proposed method utilizes a most powerful tool derivative from evolutionary game theory, which permits re-organizing the clustering attained with NMF method, making it consistent with the structure of the data set. The new propose a method to filter the clustering results obtained with the nonnegative matrix factorization (NMF) technique, imposing consistency constraints on the final labeling of the data set. The research community focused its effort on the initialization and on the optimization part of this method, without paying concentration to the final cluster assignments. The propose a game theoretic framework in which each object to be clustered is symbolized as a player, which has to choose its cluster membership. The detailed obtained with NMF method is used to initialize the approach space of the players and a weighted graph is worn to model the interactions among all the players. These connections allow the players to choose a cluster which is coherent with the clusters chosen by similar players, a property which is not guaranteed by NMF, since it produces a soft clustering of the data. The proposed results on common benchmarks show that our model is able to progress the performances of many NMF formulations.

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Keywords
Enter Data Cluster; Nonnegative Matrix Factorization; Weighted Graph ; Game Theoretic.