TY - GEN
T1 - K-DBSCAN
T2 - 2015 International Workshop on Data Mining with Industrial Applications, DMIA 2015
AU - Debnath, Madhuri
AU - Tripathi, Praveen Kumar
AU - Elmasri, Ramez
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/8/16
Y1 - 2016/8/16
N2 - Spatial clustering is a very important tool in the analysis of spatial data. In this paper, we propose a novel density based spatial clustering algorithm called K-DBSCAN with the main focus of identifying clusters of points with similar spatial density. This contrasts with many other approaches, whose main focus is spatial contiguity. The strength of K-DBSCAN lies in finding arbitrary shaped clusters in variable density regions. Moreover, it can also discover clusters with overlapping spatial regions, but differing density levels. The goal is to differentiate the most dense regions from lower density regions, with spatial contiguity as the secondary goal. The original DBSCAN fails to discover the clusters with variable density and overlapping regions. OPTICS and Shared Nearest Neighbour (SNN) algorithms have the capabilities of clustering variable density datasets but they have their own limitations. Both fail to detect overlapping clusters. Also, while handling varying density, both of the algorithms merge points from different density levels. K-DBSCAN has two phases: first, it divides all data objects into different density levels to identify the different natural densities present in the dataset, then it extracts the clusters using a modified version of DBSCAN. Experimental results on both synthetic data and a real-world spatial dataset demonstrate the effectiveness of our clustering algorithm.
AB - Spatial clustering is a very important tool in the analysis of spatial data. In this paper, we propose a novel density based spatial clustering algorithm called K-DBSCAN with the main focus of identifying clusters of points with similar spatial density. This contrasts with many other approaches, whose main focus is spatial contiguity. The strength of K-DBSCAN lies in finding arbitrary shaped clusters in variable density regions. Moreover, it can also discover clusters with overlapping spatial regions, but differing density levels. The goal is to differentiate the most dense regions from lower density regions, with spatial contiguity as the secondary goal. The original DBSCAN fails to discover the clusters with variable density and overlapping regions. OPTICS and Shared Nearest Neighbour (SNN) algorithms have the capabilities of clustering variable density datasets but they have their own limitations. Both fail to detect overlapping clusters. Also, while handling varying density, both of the algorithms merge points from different density levels. K-DBSCAN has two phases: first, it divides all data objects into different density levels to identify the different natural densities present in the dataset, then it extracts the clusters using a modified version of DBSCAN. Experimental results on both synthetic data and a real-world spatial dataset demonstrate the effectiveness of our clustering algorithm.
KW - Clustering algorithm
KW - Density-based clustering
KW - Spatial data
UR - https://www.scopus.com/pages/publications/84988019207
U2 - 10.1109/DMIA.2015.14
DO - 10.1109/DMIA.2015.14
M3 - Conference contribution
AN - SCOPUS:84988019207
T3 - Proceedings - 2015 International Workshop on Data Mining with Industrial Applications, DMIA 2015: Part of the ETyC 2015
SP - 51
EP - 60
BT - Proceedings - 2015 International Workshop on Data Mining with Industrial Applications, DMIA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 September 2015 through 16 September 2015
ER -