Name
Abstract | ||
|---|---|---|
Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P- DBSCAN 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CIDM.2009.4938646 | Nashville, TN |
Keywords | Field | DocType |
data mining,P-DBSCAN clustering algorithm,Schwartzberg index,density-based polygon clustering,distance function,lower compactness index,spatial analysis,spatial data mining | OPTICS algorithm,k-medians clustering,Pattern recognition,Correlation clustering,Computer science,Determining the number of clusters in a data set,SUBCLU,Artificial intelligence,Cluster analysis,Machine learning,DBSCAN,Single-linkage clustering | Conference |
ISBN | Citations | PageRank |
978-1-4244-2765-9 | 13 | 0.85 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deepti Joshi | 1 | 62 | 5.55 |
| A Samal | 2 | 1033 | 213.54 |
| Leen-kiat Soh | 3 | 592 | 81.43 |