Date Added: May 2012
A fundamental problem that frequently arises in a great variety of fields such as pattern recognition, image processing, machine learning is the clustering problem. In its basic form the clustering problem is defined as the problem of finding homogeneous groups of data points in a given data set. Each of these groups is called a cluster in which the density of objects is locally higher than in other regions. A popular clustering method that minimizes the clustering error is the k-means algorithm. However, the k-means algorithm is well known that its performance heavily depends on the initial starting conditions.