Rough-DBSCAN: A fast hybrid density based clustering method for large data sets

Density based clustering techniques like DBSCAN are attractive because it can find arbitrary shaped clusters along with noisy outliers. Its time requirement is O(n²) where n is the size of the dataset, and because of this it is not a suitable one to work with large datasets. A solution proposed in the paper is to apply the leaders clustering method first to derive the prototypes called leaders from the dataset which along with prototypes preserves the density information also, then to use these leaders to derive the density based clusters. The proposed hybrid clustering technique called rough-DBSCAN has a time complexity of O(n) only and is analyzed using rough set theory. Experimental studies are done using both synthetic and real world datasets to compare rough-DBSCAN with DBSCAN. It is shown that for large datasets rough-DBSCAN can find a similar clustering as found by the DBSCAN, but is consistently faster than DBSCAN. Also some properties of the leaders as prototypes are formally established.