ABSTRACT

Clustering is one of the unsupervised learning method in which a set of essentials is separated into uniform groups. The k-means method is one of the most widely used clustering techniques for various 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 research, the most representative algorithms K-Means and the Enhanced K-means were examined and analyzed based on their basic approach. The best algorithm was found out based on their performance using Normal 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 enhanced k-means is less than k-means algorithm.