Density-Based Clustering of Polygons

Authors

• Deepti Joshi, University of Nebraska-LincolnFollow
• Ashok Samal, University of Nebraska-LincolnFollow
• Leen-Kiat Soh, University of Nebraska-LincolnFollow

Date of this Version

2009

Document Type

Article

Comments

Published in IEEE Symposium on Computational Intelligence and Data Mining, 2009 (CIDM '09). Copyright 2009, IEEE. Used by permission.

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. 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.