Abstract
A co-location pattern is a set of spatial features whose objects are frequently located in spatial proximity. Spatial co-location patterns resemble frequent patterns in many aspects. Since its introduction, the paradigm of mining frequent patterns has undergone a shift from a generate-and-test based frequent pattern mining to a projection based frequent pattern mining. However for spatial datasets, the lack of a transaction concept, which is critical in frequent pattern definition and its mining algorithms, makes the similar shift of paradigm in spatial co-location mining very difficult. We investigate a projection based co-location mining paradigm. In particular, we propose a projection based co-location mining framework and an algorithm called FP-CM, for FP-growth Based Co-location Miner. This algorithm only requires a small constant number of database scans. It out-performs the generate-and-test algorithm by an order of magnitude as shown by our preliminary experiment results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agarwal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int’l Conference on Very Large Data Bases (1994)
Cressie, N.A.C.: Statistics for Spatial Data. Wiley and Sons, Chichester (1991)
Estivill-Castro, V., Lee, I.: Data Mining Techniques for Autonomous Exploration of Large Volumes of Geo-referenced Crime Data. In: Proc. of the 6th International Conference on Geocomputation (2001)
Estivill-Castro, V., Murray, A.: Discovering Associations in Spatial Data - An Efficient Medoid Based Approach. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394. Springer, Heidelberg (1998)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Intl. Conference on Management of Data (2000)
DeWitt, D.J., Patel, J.M.: Partition Based Spatial-Merge Join. In: Proc. of the ACM SIGMOD Conference on Management of Data (June 1996)
Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Proc. of the 4th International Symposium on Spatial Databases (1995)
Mamoulis, N., Papadias, D.: Multiway spatial joins. ACM Trans. Database Syst. 26(4), 424–475 (2001)
Morimoto, Y.: Mining Frequent Neighboring Class Sets in Spatial Databases. In: Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
Shekhar, S., Huang, Y.: Co-location Rules Mining: A Summary of Results. In: Proc. 7th Intl. Symposium on Spatio-temporal Databases (2001)
Zhang, X., Mamoulis, N., Cheung, D.W., Shou, Y.: Fast Mining of Spatial Collocations. In: Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, Y., Zhang, L., Yu, P. (2005). Can We Apply Projection Based Frequent Pattern Mining Paradigm to Spatial Co-location Mining?. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_83
Download citation
DOI: https://doi.org/10.1007/11430919_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
eBook Packages: Computer ScienceComputer Science (R0)