Density based clustering technique for efficient data mining

Clustering analysis is an important function of data mining. There are various clustering methods in data mining. Based on these methods various clustering algorithms are developed. A recent approach for clustering analysis is based on ldquoswarm intelligencerdquo. Based on this ldquoswarm intelligencerdquo an algorithm was proposed named ldquoant-cluster algorithmrdquo. However, existing ldquoant clusteringrdquo algorithm has a limitation in finding the value of two constant K1 and K2, which is user defined., for computing the value of the picking up probability Pp and dropping probability Pd. In this paper our approach is to gain the value of Pp and Pd without giving the user defined value of K1 and K2. We also intend to retain the Pp and Pd in between 0 to 1 in order to get optimized result.