K nearest neighbor reinforced expectation maximization method

Authors:
Mehmet Aci;Mutlu Avci
Affiliations:
Department of Computer Engineering, University of Cukurova, Adana, Turkey;Department of Computer Engineering, University of Cukurova, Adana, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

K nearest neighbor and Bayesian methods are effective methods of machine learning. Expectation maximization is an effective Bayesian classifier. In this work a data elimination approach is proposed to improve data clustering. The proposed method is based on hybridization of k nearest neighbor and expectation maximization algorithms. The k nearest neighbor algorithm is considered as the preprocessor for expectation maximization algorithm to reduce the amount of training data making it difficult to learn. The suggested method is tested on well-known machine learning data sets iris, wine, breast cancer, glass and yeast. Simulations are done in MATLAB environment and performance results are concluded.