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K means algorithm is most popular partition based algorithm that is widely used in data clustering. A Lot of algorithms have been proposed for data clustering using K-Means algorithm due to its simplicity, efficiency and ease convergence. In spite this K-Means algorithm has some drawbacks like initial cluster centers, stuck in local optima etc. In this study, a new method is proposed to address the initial cluster centers problem in K-Means algorithm based on binary search technique. Binary search technique is a popular searching method that is used to find an item in given list of array. So in proposed method, the initial cluster centers have obtained using binary search property and after that K-Means algorithm is applied to gain optimal cluster centers in dataset. The performance of the proposed algorithm is tested on the two benchmark dataset which are downloaded from the UCI machine learning repository and compared with Random, Hartigan and Wang, Ward, Build, Astrhan and Minkowaski ward methods. The proposed method is also applied on the Minkowaski weighted K-Means algorithm to prove its significance and effectiveness.

A New Initialization Method to Originate Initial Cluster Centers for K-Means Algorithm