Abstract
Advances in sensing and satellite technologies and the growth of Internet have resulted in the easy accessibility of vast amount of spatial data. Extracting useful knowledge from these data is an important and challenging task, in particular, finding interaction among spatial features. Existing works typically adopt a grid-like approach to transform the continuous spatial space to a discrete space. In this paper, we propose to model the spatial features in a continuous space through the use of influence functions. For each feature type, we build an influence map that captures the distribution of the feature instances. Superimposing the influence maps allows the interaction of the feature types to be quickly determined. Experiments on both synthetic and real world datasets indicate that the proposed approach is scalable and is able to discover patterns that have been missed by existing methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gouda, K., Zaki, M.J.: Efficiently mining maximal frequent itemsets. In: ICDM, pp. 163–170 (2001)
Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering 16(12), 1472 (2004)
Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Morimoto, Y.: Mining frequent neighboring class sets in spatial databases. In: ACM SIGKDD, pp. 353–358 (2001)
Ripley, B.D.: Spatial Statistics. Wiley, Chichester (1981)
Bayardo, J. R.J.: Efficiently mining long patterns from databases. In: SIGMOD 1998: Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp. 85–93. ACM Press, New York (1998)
Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, Chichester (1992)
Sheng, C., Hsu, W., Lee, M.L.: Discovering spatial interaction patterns. Technique Report TRC6/07. National University of Singapore (June 2007)
Wang, J., Hsu, W., Lee, M.L.: A framework for mining topological patterns in spatio-temporal databases. In: ACM CIKM 2005, pp. 429–436. ACM Press, New York (2005)
Yoo, J.S., Shekhar, S., Celik, M.: A join-less approach for co-location pattern mining: A summary of results. In: ICDM, pp. 813–816 (2005)
Zhang, X., Mamoulis, N., Cheung, D.W., Shou, Y.: Fast mining of spatial collocations. In: ACM SIGKDD 2004, pp. 384–393. ACM Press, New York (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sheng, C., Hsu, W., Lee, M.L., Tung, A.K.H. (2008). Discovering Spatial Interaction Patterns. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds) Database Systems for Advanced Applications. DASFAA 2008. Lecture Notes in Computer Science, vol 4947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78568-2_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-78568-2_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78567-5
Online ISBN: 978-3-540-78568-2
eBook Packages: Computer ScienceComputer Science (R0)