Authors: Abbas H. Hassin Alasadi; Moslem Mohsinn Khudhair
Addresses: Department of Computer Science, College of Science, Basra University, Iraq ' Department of Computer Science, College of Science, Basra University, Iraq
Abstract: The traditional k-means algorithm is a classical clustering method which is widely used in variant application such as image processing, computer vision, pattern recognition and machine learning. However, the k-means method converges to one of many local minima. It is known that, the final result depends on the initial starting points (means). Generally initial cluster centres are selected randomly, so the algorithm could not lead to the unique result. In this paper, we present a new algorithm which includes three methods to compute initial centres for k-means clustering. First one is called geometric method which depends on equal areas of distribution. The second is called block method which segments the image into uniform areas. The last method is called hybrid and it is a combination between first and second methods. The experimental results appeared quite satisfactory.
Keywords: hybrid clustering; k-means clustering; image segmentation; colour images; modelling.
International Journal of Reasoning-based Intelligent Systems, 2012 Vol.4 No.4, pp.245 - 249
Published online: 27 Jan 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article