Abstract
The majority of clustering algorithms deal with collections of data that can be represented as sets of points in the multidimensional Euclidean space. There is a large variety of application domains, such as spatiotemporal databases, medical applications and others, which produce datasets of non-point objects (i.e. objects that occupy a specific hyperspace). Traditional clustering algorithms are mainly based on statistical properties of data and therefore are not able to efficiently partition sets of spatially extended objects.
In this paper we propose NPClu, an approach for clustering sets of objects taken into account their geometric and topological properties. The spatial objects are approximated by their MBRs. Then our approach discovers the clusters in the set of the MBRs' vertices based on three steps, that is, pre-processing, clustering and refinement. We experimentally evaluated the performance of our approach to show its effectiveness.