This paper introduces DBSCAN-MO, an algorithm for density-based clustering of point objects on a planar surface with moving obstacles. This algorithm extends a well known spatial clustering method, named DBSCAN, which has been initially proposed to cluster point objects in a static space. DBSCAN-MO is able to form a set of spatio-temporal clusters and may be readily customized to complex dynamic environments. A prototype system, which implements the algorithm, developed in Java and tested through a series of synthetic datasets, is also presented.
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References
Allen, J.F., (1983) Maintaining Knowledge about Temporal Intervals. Communications of the ACM, 26(11), pp. 832-843.
Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases. In the Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD’96). Portland, Oregon, pp. 226-231.
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Ulthurusamy, R. (Eds) (1996) Advances in Knowledge Discovery and Data Mining. MIT Press.
Goebel, M., and Gruenwald, L. (1999) A Survey of Data Mining and Knowledge Discovery Software Tools. SIGKDD Explorations (1), ACM, pp. 20-33.
Han, J., Kamber, M., and Tung, A.K.H. (2001) Spatial Clustering Methods in Data Mining: A Survey. In Miller and Han 2001.
Laurini, R., and Thompson, D. (1992) Fundamentals of Spatial Information Systems. Academic Press Ltd.
Manolopoulos, Y., Nanopoulos, A., Papadopoulos A.N., and Theodoridis, Y. (2005) R-trees: Theory and Applications. Series in Advanced Information and Knowledge Processing, Springer.
Miller, H.J and Han, J. (Eds) (2001) Geographic Data Mining and Knowledge Discovery. Taylor & Francis.
Samet, H. (1990) The Design and Analysis of Spatial Data Structures. Addison-Wesley, Reading MA.
Sander, J., Ester, M., Kriegel, H.P., and Xu, X., (1998) Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications. Data Mining and Knowledge Discovery, 2(2), pp. 169-194.
Sedgewick, R. (1990) Algorithms. Addison-Wesley.
Stefanakis, E. (2004) Navigating among Moving Obstacles. In the Proceedings of the 3rd International Conference on Geographic Information Science (GIScience 2004), Adelphi, MD, Oct. 20-23, 2004.
Stefanakis, E. (2005) Clustering Dynamic Map Objects Based on Density Measures. In Proceedings of the 22nd International Cartographic Conference. A Coruna, Spain, July 2005.
Stefanakis, E. (2006) Scheduling Trajectories on a Planar Surface with Moving Obstacles. Informatica. Vol. 17(1), pp. 95-110.
Stefanakis, E. (2007) NET-DBSCAN: Clustering the Nodes of a Dynamic Linear Network. International Journal of Geographical Information Science. Taylor & Francis. Volume 21(4), 427-442.
Stefanakis, E., and Kavouras, M. (1995) On the Determination of the Optimum Path in Space, In Frank, A., and Kuhn, W., (Ed’s.), Spatial Information Theory: A Theoretical Basis for GIS (COSIT 95). Springer-Verlag, pp. 241-257.
Stefanakis, E., and Kavouras, M. (2002) Navigating in Space under Constraints, International Journal of Pure and Applied Mathematics (IJPAM), Vol. 1(1), Academic Publ., pp. 71-93.
Worboys, M.F. (1995) GIS: A Computing Perspective. Taylor & Francis.
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Stefanakis, E. (2008). DBSCAN-MO: Density-Based Clustering among Moving Obstacles. In: Bernard, L., Friis-Christensen, A., Pundt, H. (eds) The European Information Society. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78946-8_9
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