• AN ENHANCED ALGORITHM FOR IMPROVING THE EFFICIENCY OF K-MEANS AND K-MEDOID CLUSTERING USING NORMAL DISTRIBUTION DATA POINTS

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. 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 K-Medoids 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 Normal Distribution data points. The accuracy of the algorithm was investigated during different execution of the program using Normal Distribution input data points. colors and the execution time is calculated in milliseconds. This paper deals with a method for improving efficiency of the K-Means algorithm and analyze the elapsed time is taken by Efficient K-Means is less than K-Means and K-Medoid algorithm.