Revised DBSCAN algorithm to cluster data with dense adjacent clusters

Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely used in many areas of science due to its simplicity and the ability to detect clusters of different sizes and shapes. However, the algorithm becomes unstable when detecting border objects of adjacent clusters as was mentioned in the article that introduced the algorithm. The final clustering result obtained from DBSCAN depends on the order in which objects are processed in the course of the algorithm run. In this article, a modified version of the DBSCAN algorithm is proposed to solve this problem. It was shown that by using the revised algorithm the clustering results are considerably improved, in particular for data sets containing dense structures with connected clusters. (C) 2012 Elsevier B.V. All rights reserved.