TY - GEN
T1 - Density-based clustering of polygons
AU - Joshi, Deepti
AU - Samal, Ashok K.
AU - Soh, Leen Kiat
PY - 2009
Y1 - 2009
N2 - Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. PDBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by using a distance function customized for the polygon space. The objective of our clustering algorithm is to produce spatially compact clusters. We measure the compactness of the clusters produced using P-DBSCAN and compare it with the clusters formed using DBSCAN, using the Schwartzberg Index. We measure the effectiveness and robustness of our algorithm using a synthetic dataset and two real datasets. Results show that the clusters produced using P-DBSCAN have a lower compactness index (hence more compact) than DBSCAN.
AB - Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. PDBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by using a distance function customized for the polygon space. The objective of our clustering algorithm is to produce spatially compact clusters. We measure the compactness of the clusters produced using P-DBSCAN and compare it with the clusters formed using DBSCAN, using the Schwartzberg Index. We measure the effectiveness and robustness of our algorithm using a synthetic dataset and two real datasets. Results show that the clusters produced using P-DBSCAN have a lower compactness index (hence more compact) than DBSCAN.
UR - http://www.scopus.com/inward/record.url?scp=67650469122&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650469122&partnerID=8YFLogxK
U2 - 10.1109/CIDM.2009.4938646
DO - 10.1109/CIDM.2009.4938646
M3 - Conference contribution
AN - SCOPUS:67650469122
SN - 9781424427659
T3 - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
SP - 171
EP - 178
BT - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
T2 - 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009
Y2 - 30 March 2009 through 2 April 2009
ER -