The MinMax k-means algorithm is widely used to tackle the effect of bad initialization by minimizing the maximum intraclustering errors. Two parameters, including the exponent parameter and memory parameter, are involved in the executive process. Since different parameters have different clustering errors, it is crucial to choose appropriate parameters. In the original algorithm, a practical fr...

The k-means clustering algorithm is most popularly used in data mining for real world applications. The efficiency and performance of the k-means algorithm is greatly affected by initial cluster centers as different initial cluster centers often lead to different clustering. In this paper, we propose a modified k-means algorithm which has additional steps for selecting better cluster centers. W...

One con transpor pumps’. *Autho Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK Laboratoire de Biologie et Physiopathologie des Systèmes Intégrés, CNRS-Universite de Nice, FRE 3094, Parc Valrose, Nice Cedex 2, France Bristol Institute for Transfusion Sciences, National Blood Service, Southmead, Bristol BS10 5ND, UK Department of Medicine, University Coll...

Image segmentation is used to recognizing some objects or something that is more meaningful and easier to analyze In this paper we are focus on the the K means clustering for segmentation of the image. K-means clustering is the most widely used clustering algorithm to position the radial basis function (RBF) centres. Its simplicity and ability to perform on-line clustering may inspire this choi...

The proliferation of internet along with the attractiveness of the web in recent years has made web mining as the research area of great magnitude. Web mining essentially has many advantages which makes this technology attractive to researchers. The analysis of web user’s navigational pattern within a web site can provide useful information for applications like, server performance enhancements...