Abstract

Abstract: Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human’s ability to analyze. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial database. The database can be clustered in many ways depending on the clustering algorithm employed, parameter settings used, and other factors. Multiple clustering can be combined so that the final partitioning of data provides better clustering. In this paper, a novel density based k-means clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and kmeans clustering algorithms. The result will be an improved version of k-means clustering algorithm. This algorithm will perform better than DBSCAN while handling clusters of circularly distributed data points and slightly overlapped clusters.

Authors and Affiliations