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K nearest neighbor and Bayesian algorithms are effective methods of machine learning. 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 Bayesian learning algorithms. The suggested method is tested on well-known machine learning data sets such as iris, wine and breast cancer and the results are concluded.