A roadmap to varied density dataset issue of DBSCAN and its variants

Wide variety of methods had been designed under the cluster analysis; an unsupervised learning, like partitioning based, hierarchical, density based, model based, etc. DBSCAN, one of the most widely applied density based clustering algorithm outperforms partitioning based clustering algorithms such as k-means, CLARA, CLARANS and hierarchical algorithms, as it does not require a prior knowledge of number of clusters or termination condition and generates clusters of arbitrary shape, which need not to be convex. Despite the wide applicability, it also exhibits few issues like: i) time complexity is O (n) if R* indexing is not used, ii) does not work properly for the varying density dataset and iii) Eps and MinPts, two input parameters selection greatly change the output. To overcome these issues different modifications of original DBSCAN had been proposed in the literature. The algorithms proposed for handling varied density dataset are surveyed in this paper.