Critical Analysis of Density-based Spatial Clustering of Applications with Noise (DBSCAN) Techniques

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Clustering is the most used technique in data mining. Clustering maximize the intra-cluster similarity and minimize the inter clusters similarity. DBSCAN is the basic density based clustering algorithm. Cluster is defined as regions of high density are separated from regions that are less dense. DBSCAN algorithm can discover clusters of arbitrary shapes and size in large spatial databases. Beside its popularity, DBSCAN has drawbacks that its worst time complexity reaches to O (n2). Similarly, it cannot deal with varied densities. It is hard to know the initial value of input parameters. In this study, we have studied and discussed some significant enhancement of DBSCAN algorithm to tackle with these problems. We analysed all the enhancements to computational time and output to the original DBSCAN. Majority of variations adopted hybrid techniques and use partitioning to overcome the limitations of DBSCAN algorithm. Some of which performs better and some have their own usefulness and characteristics.