International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 113 | Views: 183

Research Paper | Computer Science & Engineering | India | Volume 3 Issue 6, June 2014


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

A. Mallikarjuna Reddy | Ramapuram Gautham


Abstract: 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.


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


Edition: Volume 3 Issue 6, June 2014,


Pages: 764 - 768


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