Conference Proceedings

International Conference on Advances in Computer and Information Technology - ACIT 2013

An Approach for Selecting Optimal Initial Centroids to Enhance the Performance of K-means

Author(s) : MD. MOSTAFIZER RAHMAN, MD. SOHRAB MAHMUD, MD.NASIM AKHTAR

Abstract

Clustering is the process of grouping data into a set of disjoint classes called cluster. It is an effective technique used to classify collection of data into groups of related objects. K-means clustering algorithm is one of the most widely used clustering techniques. The main puzzle of K-means is initialization of centroids. Clustering performance of the K-means totally depends upon the correctness of the initial centroids. In general, K-means randomly selects initial centroids which often show in poor clustering results. This paper has proposed a new approach to optimizing the designation of initial centroids for K-means clustering. We propose a new approach for selecting initial centroids of K-means based on the weighted score of the dataset. According to our experimental results the new approach of K-means clustering algorithm reduces the total number of iterations, improve the time complexity and also it has the higher accuracy than the standard k-means clustering algorithm.

Conference Title : International Conference on Advances in Computer and Information Technology - ACIT 2013
Conference Date(s) : May 04-05, 2013
Place : Hotel Shangri-La, Kuala Lumpur, Malaysia
No fo Author(s) : 3
DOI : 10.15224/978-981-07-6261-2-32
Page(s) : 152 - 156
Electronic ISBN : 978-981-07-6261-2
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