Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster

Wan Maseri, Wan Mohd and Beg, Abul Hashem and Tutut, Herawan and K., F.Rabbi Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster. Fundamental Research Grant Scheme. pp. 15-21. (Published)

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Abstract

K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. The main drawback of this algorithm is that user should specify the number of cluster in advance. As an iterative clustering strategy, K-Means algorithm is very sensitive to the initial starting conditions. In this paper has been proposed a clustering technique called MaxD K-Means clustering algorithm. MaxD K-Means algorithm auto generates initial k (the desired number of cluster) without asking for input from the user. MaxD k-means also used a novel strategy of setting the initial centroids. The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. Another experiment has been done using reallife data focusing on student’s results in higher-education institution in Malaysia. The results from the new algorithm show that the number of iteration improves tremendously, and the number of iterations is reduced. The improvement rate is around 78%.

Item Type:	Article
Uncontrolled Keywords:	K-means algorithm, Partitioning algorithm, Clustering, MaxD kmeans, Data mining.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Ms. Hazima Anuar
Date Deposited:	05 Jan 2015 03:31
Last Modified:	14 Sep 2017 03:43
URI:	http://umpir.ump.edu.my/id/eprint/6871
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