A Novel Approach to Clustering Algorithms and Their Comparative Performance Analysis on Different Data Set

Abstract

K-means is the basic algorithm used for discovering clusters within a dataset. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy, performance and computational time are improved. Some enhanced variations improve the efficiency and accuracy of the algorithm. Basically, in all the methods, the main aim is to reduce the number of iterations which will decrease the computational time. Studies show that K-means algorithm in clustering is widely used technique. Various enhancements done on k-mean are collected, so by using these enhancements, one can build a new hybrid algorithm which will be more efficient, accurate and less time consuming than the previous work.