Analyse k-means algorithm and implementing a new clustering algorithm

Abstract

Clustering is a technique of machine learning which involves grouping data points. We can use clustering algorithm to cluster each data points into a specific group. K-means algorithm is the most famous and commonly used algorithm for analysis of clusters. The k-means clustering algorithm is a method of partitioning clusters that partition data objects into k different clusters. However, the main drawback of this algorithm is that classical k-means algorithm is mainly sensitive to initial centroids and it is also difficult to determine the total number of clusters. So in order to improve the performance of k-means algorithm, this paper presents a new modified k-means method which uses intra-class distance to cluster the data.