Title: Efficient clustering technique for regionalisation of a spatial database

Authors: Lokesh Kumar Sharma, Simon Scheider, Willy Kloesgen, Om Prakash Vyas

Addresses: Fraunhofer Institut Intelligente Analyse und Informationssysteme, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. ' Fraunhofer Institut Intelligente Analyse und Informationssysteme, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. ' Fraunhofer Institut Intelligente Analyse und Informationssysteme, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. ' Department of Computer Science, Pt. Ravishankar Shukla University, Raipur 492010, India

Abstract: Regionalisation, a prominent problem from social geography, could be solved by a classification algorithm for grouping spatial objects. A typical task is to find spatially compact and dense regions of arbitrary shape with a homogeneous internal distribution of social variables. Grouping a set of homogeneous spatial units to compose a larger region can be useful for sampling procedures as well as many applications, e.g., direct mailing. It would be helpful to have specific purpose regions, depending on the kind of homogeneity one is interested in. In this paper, we propose an algorithm combining the |spatial density| clustering approach and a covariance-based method to inductively find spatially dense and non spatially homogeneous clusters of arbitrary shape.

Keywords: regionalisation; spatial data mining; density-based cluster; efficient clustering; spatial density; spatial database.

DOI: 10.1504/IJBIDM.2008.017976

International Journal of Business Intelligence and Data Mining, 2008 Vol.3 No.1, pp.66 - 81

Available online: 25 Apr 2008 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article