Clustering Spatial Data when Facing Physical Constraints

Clustering spatial data is a well-known problem that has been extensively studied to find hidden patterns or meaningful sub-groups and has many applications such as satellite imagery, geographic information systems, medical image analysis, etc. Although many methods have been proposed in the literature, very few have considered constraints such that physical obstacles and bridges linking clusters may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly, and the effective modeling of the constraints is of paramount importance for good performance. In this paper, we define the clustering problem in the presence of constraints – obstacles and crossings – and investigate its efficiency and effectiveness for large databases. In addition, we introduce a new approach to model these constraints to prune the search space and reduce the number of polygons to test during clustering. The algorithm DBCluC we present detects clusters of arbitrary shape and is insensitive to noise and the input order. Its average running complexity is O(NlogN) where N is the number of data