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

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.