An Efficient Method for subjectively choosing parameter 'k' automatically in VDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise) Algorithm

Density based clustering algorithms are one of the primary method for data mining. The clusters which are formed using density clustering are easy to understand and it does limit itself to shapes of clusters. Existing density based algorithms have trouble because they are not capable of finding out all 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.