Abstract
Recently, there has been considerable interest in mining spatial colocation patterns from large spatial datasets. Spatial colocations represent the subsets of spatial events whose instances are frequently located together in nearby geographic area. Most studies of spatial colocation mining require the specification of a minimum prevalent threshold to find the interesting patterns. However, it is difficult for users to provide appropriate thresholds without prior knowledge about the task-specific spatial data. We propose a different framework for spatial colocation pattern mining: finding N-most prevalent colocated event sets, where N is the desired number of event sets with the highest interest measure values per each pattern size. We developed an algorithm for mining N-most prevalent colocation patterns. Experimental results with real data show that our algorithmic design is computationally effective.
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References
Arshad, M.U., Ayyaz, M.N.: Mining N-most Iteresting Itemsets using Support-Ordered Tries. In: IEEE Int’l Conf. on Computer Systems and Applications (2006)
Cheung, Y., Fu, A.W.: Mining Frequent Itemsets without Support Threshold: With and Without Item Constraints. IEEE Transactions on Knowledge and Data Engineering 16(9) (2004)
Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms. McGraw-Hill Science, New York (2003)
Ding, W., Jiamthapthaksin, R., Parmar, R., Jiang, D., Stepinski, T.F., Eick, C.F.: Towards Region Discovery in Spatial Datasets. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS, vol. 5012, pp. 88–99. Springer, Heidelberg (2008)
Eick, C.F., Parmar, R., Ding, W., Stepinski, T.F., Nicot, J.: Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets. In: Proc. of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(ACM-GIS) (2008)
Fu, A.W., Kwong, R.W., Tang, J.: Mining N-most Interesting Itemsets. In: International Syposium on Methodologies for Intelligent Systems (2000)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns Without Candidate Generation. In: Proc. of ACM SIGMOD Conference on Management of Data (2000)
Hirate, Y., Iwahashi, E., Yamana, H.: TF 2 P-growth: An Efficient Algorithm for Mining Frequent Patterns without any Thresholds. In: Proc. of Workshop on Alternative Techniques for Data Mining and Knowledge Discovery (2004)
Huang, Y., Zhang, L., Yu, P.: Can we apply projection based frequent pattern mining paradigm to spatial co-location mining? In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 719–725. Springer, Heidelberg (2005)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data. In: Proc. of European Conference on Principles of Data Mining and Knowledge Discovery (2000)
Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. In: Proc. of International Symposium on Large Spatial Data bases, Maine, pp. 47–66 (1995)
Kuramochi, M., Karypis, G.: Fequent Subgraph Discovery. In: IEEE International Conference on Data Mining (2001)
Li, F., Cheng, D., Hadjieleftheriou, M., Kollios, G., Teng, S.: On Trip Planning Queries in Spatial Databases. In: Proc. of Interational Symposium on Advances in Spatial and Temporal Databases(SSTD) (2005)
Morimoto, Y.: Mining Frequent Neighboring Class Sets in Spatial Databases. In: Proc. ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (2001)
Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice-Hall, Englewood Cliffs (2003)
Shekhar, S., Huang, Y.: Co-location Rules Mining: A Summary of Results. In: Proc. of International Symposium on Spatial and Temporal Database, SSTD (2001)
Wang, J., Han, J., Lu, Y., Tzvetkov, P.: TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets. IEEE Transactions on Knowledge and Data Engineering 17(5) (2005)
Xiao, X., Xie, X., Luo, Q., Ma, W.: Density based co-location pattern discovery. In: Proc. of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM-GIS) (2008)
Yan, X., Han, J.: gSpan: Graph-Baseed Substructure Pattern Mining. In: IEEE International Conference on Data Mining (2001)
Yoo, J.S., Bow, M.: Finding N-Most Prevalent Colocated Event Sets: A Summary of Results. Technical Report (2009), http://users.ipfw.edu/yooj/publications/NMost-Summary.pdf
Yoo, J.S., Shekhar, S.: A Partial Join Approach for Mining Co-location Patterns. In: Proc. of ACM ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems(ACM-GIS) (2004)
Yoo, J.S., Shekhar, S.: A Join-less Approach for Mining Spatial Co-location Patterns. IEEE Transactions on Knowledge and Data Engineering 18(10) (2006)
Zou, L., Chen, L., Lu, Y.: Top-k Subgraph Matching Query in a Large Graph. In: Proc. of Conference on Information and Knowledge Management (2000)
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Yoo, J.S., Bow, M. (2009). Finding N-Most Prevalent Colocated Event Sets. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2009. Lecture Notes in Computer Science, vol 5691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03730-6_33
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DOI: https://doi.org/10.1007/978-3-642-03730-6_33
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