NPClu: An approach for clustering spatially extended objects

Article type: Research Article

Authors: Halkidi, Maria^{; *} | Vazirgiannis, Michalis

Affiliations: Department of Informatics, Athens University of Economics & Business, 76 Patision Street, Athens 104 34, Greece

Correspondence: [*] Corresponding author: Tel.: +30 210 8203515; Fax: +30 210 8203517; E-mail: [email protected], [email protected].

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.

Keywords: Spatial clustering, unsupervised learning, spatial data mining

DOI: 10.3233/IDA-2008-12605

Journal: Intelligent Data Analysis, vol. 12, no. 6, pp. 587-606, 2008