An efficient K-Means clustering algorithm for reducing time complexity using uniform distribution data points

Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm more effective and efficient; so as to get better clustering with reduced complexity. In this paper, the most delegate algorithms K-Means and proposed K-Means were examined and analyzed based on their basic approach. The best algorithm in each category was found out based on their performance using uniform distribution data points. The accuracy of the algorithm was investigated during different execution of the program on the input data points. The elapsed time taken by proposed efficient K-Means is less than K-Means algorithm.