Random automatic detection of clusters

Clustering is a way to partition the database in various groups. It is being used in data mining at a very large scale. There are different clustering methods but the focus in this paper is on partitioning based clustering. In literature many algorithm including k-Means are available that require prior information from the outside world about the number of clusters into which the database is to be divided. However, now days a database requires such algorithms that can generate different clusters automatically and moreover at each run the database requires to be partitioned into different number of clusters as well as different shape and size of grouping. In this paper a new partitioning based clustering algorithm that can generate clusters automatically without any previous knowledge on the user side has been proposed. The clusters so generated may not only differ in number but also will be of different shape and size.