Comparative analysis of clustering of spatial databases with various DBSCAN Algorithms
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2012, Vol 1, Issue 6
Abstract
Clustering is still an important research issue in the data mining, because there is a continuous research in data mining for optimum clusters on spatial data. There are so many types of partition based and hierarchal algorithms implemented for clustering and the clusters which are formed based on the density are easy to understand and it does not limit itself to certain shapes of the clusters. This paper presents the comparative analysis of the various density based clustering mechanisms. There is certain problem on existing density based algorithms because they are not capable of finding the meaningful clusters whenever the density is so much varied. VDBSCAN is introduced to compensate this problem. It is same as DBSCAN (Density Based Spatial Clustering of Applications with Noise) but only the difference is VDBSCAN selects several values of parameter Eps for different densities according to k-dist plot. The problem is the value of parameter k in k-dist plot is user defined. This paper introduces a new method to find out the value of parameter k automatically based on the characteristics of the datasets. In this method we consider spatial distance from a point to all others points in the datasets. The proposed method has potential to find out optimal value for parameter k .In this paper a synthetic database with two dimensional data is used for demonstration.
Authors and Affiliations
K. Ganga Swathi, K N V S S K Rajesh
Keywords
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