Abstract
Clustering techniques are used for the partition of the data points in clusters. In DBSCAN clustering algorithm, it deals with dense data points, but DBSCAN algorithm does not deal with varied density data. So, for variable density, VDBSCAN algorithm is suitable, since the existing VDBSCAN algorithm is unable to find the exact radius. The existing algorithm VDBSCAN is based on distance. Due to more distance and large data sets, some data points cannot become the part of any cluster. To overcome this problem, the map-reduce technique is used. Using map reduce, the values of k can be identified correctly. It provides a proper value of k on the basis of frequency. This new approach is relatively more effective than VDBSCAN.
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