Modified Centroid Selection Method of K-Means Clustering
Rose Mawati, I Made Sumertajaya, Farit Mochamad Afendi · IOSR Journal of Mathematics · 2014
Clustering is a process of classifying object into groups which have similarity.The result of clustering will show that objects in one cluster will be more homogeneous than others.There are two methods in classic clustering analysis i.e. hierarchical cluster method and non-hierarchical cluster method.Determination of the member of clusters which formed by them is done subjectively.K-means is one of the algorithms that solve the well known clustering problem.The algorithm classifies object to a predefined number of clusters, which is given by the user.The idea is to choose random cluster centers, one for each other.The centroid initialization plays an important role in determining the cluster assignment in effective ways.This paper presents results of the simulated data of different datasets using original k-means and other modified algorithms implemented using MATLAB R2010a.This results are calculated on some performance measures such as no.iterations, and accuracy.