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An Optimum Method for Enhancing the Computational Complexity of K-Means Clustering Algorithm with Improved Initial Centers

Journal: International Journal of Science and Research (IJSR) (Vol.3, No. 6)

Publication Date:

Authors : ; ;

Page : 764-768

Keywords : Mining; Clustering; Knowledge Discovery in Databases; K-means clustering algorithm; Optimum method.;

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The amount of data stored in databases continues to grow fast. Intuitively, this large amount of stored data contains valuable hidden patterns, which could be used to improve the decision-making process. Data mining is a process of identifying specific patterns from large amount of data. In data mining, Clustering is one of the major data analysis methods and the K-means clustering algorithm is widely used for many practical applications. Though it is widely used, its generates a local optimal solution based on the randomly chosen initial centroids and the computational complexity is very high O(nkl). In order to improve the performance of the K-means algorithm several methods have been proposed in the literature. The proposed algorithm enhances the performance of K-means clustering algorithm. This algorithm consists of two phases. Phase I algorithm finds the better initial centroids, Phase II algorithm is used for the effective way of assigning data points to suitable clusters. Experiments on a number of real-world data sets show that the proposed approach has produces consistent clusters compared to some well-known methods, reducing the computational complexity O(nlogn) of k-means algorithm.Though the proposed method will improve the accuracy and efficiency of k-means clustering algorithm.

Last modified: 2014-06-24 18:02:44