MULTI-DENSITY DBSCAN USING REPRESENTATIVES: MDBSCAN-UR.

Abstract

DBSCAN is one of the most popular algorithms for cluster analysis. It can discover clusters with arbitrary shape and separate noises. But this algorithm cannot choose its parameter according to distributing of dataset. It simply uses the global uses minimum number of points (MinPts) parameter, so that the clustering result of multi-density database is inaccurate. In addition, when it is used to cluster large databases, it will cost too much time. For these problems, we propose MDBSCAN-UR algorithm which is based on spatial index and grid technique witch make the clustering using representatives points that capture the shape and extent of the cell that they chosen in and that in order to enhance the time complexity. In this paper, we apply an unsupervised machine learning approach based on DBSCAN algorithm. We use local MinPts for every cell in the grid to overcome the problem of undetermined the clusters in …