l-DBSCAN: A Fast Hybrid Density Based Clustering Method

Authors:
P. Viswanath;Rajwala Pinkesh
Affiliations:
Indian Institute of Technology - Guwahati, India;Indian Institute of Technology - Guwahati, India
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Year:
2006

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Abstract

Density based clustering techniques like DBSCAN can find arbitrary shaped clusters along with noisy outliers. A severe drawback of the method is its huge time requirement which makes it a unsuitable one for large data sets. One solution is to apply DBSCAN using only a few selected prototypes. But because of this the clustering result can deviate from that which uses the full data set. A novel method proposed in the paper is to use two types of prototypes, one at a coarser level meant to reduce the time requirement, and the other at a finer level meant to reduce the deviation of the result. Prototypes are derived using leaders clustering method. The proposed hybrid clustering method called l-DBSCAN is analyzed and experimentally compared with DBSCAN which shows that it could be a suitable one for large data sets.