AN AUTOMATIC AND STABLE CLUSTERING-ALGORITHM

A new non-hierarchical and non-conventional clustering algorithm has been developed to remove the effect of the initial guess (seed points) on the performance and convergence of the ''Moving Centroid Method''. The classification is also least affected on the ordering of the data presented for clustering. The most appropriate cluster centres are obtained by studying the class strings of the initial partitions, which are generated with the help of random integers. These stable cluster centres lead to the stable partitions in a unsupervised manner.