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Discovering Colocation Patterns from Spatial Data Sets: A General Approach
December 2004 (vol. 16 no. 12)
pp. 1472-1485
ASCII Text
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Yan Huang, Shashi Shekhar, Hui Xiong, "Discovering Colocation Patterns from Spatial Data Sets: A General Approach," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1472-1485, December, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2004.90,
author = {Yan Huang and Shashi Shekhar and Hui Xiong},
title = {Discovering Colocation Patterns from Spatial Data Sets: A General Approach},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {16},
number = {12},
issn = {1041-4347},
year = {2004},
pages = {1472-1485},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.90},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IS - 12
SN - 1041-4347
SP1472
EP1485
EPD - 1472-1485
A1 - Yan Huang,
A1 - Shashi Shekhar,
A1 - Hui Xiong,
PY - 2004
KW - Colocation patterns
KW - spatial association rules
KW - participation index.
VL - 16
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Yan Huang, IEEE Computer Society
Shashi Shekhar, IEEE Computer Society
Hui Xiong, IEEE Computer Society
Given a collection of Boolean spatial features, the colocation pattern discovery process finds the subsets of features frequently located together. For example, the analysis of an ecology data set may reveal symbiotic species. The spatial colocation rule problem is different from the association rule problem since there is no natural notion of transactions in spatial data sets which are embedded in continuous geographic space. In this paper, we provide a transaction-free approach to mine colocation patterns by using the concept of proximity neighborhood. A new interest measure, a participation index, is also proposed for spatial colocation patterns. The participation index is used as the measure of prevalence of a colocation for two reasons. First, this measure is closely related to the {\rm{cross}}{\hbox{-}}K function, which is often used as a statistical measure of interaction among pairs of spatial features. Second, it also possesses an antimonotone property which can be exploited for computational efficiency. Furthermore, we design an algorithm to discover colocation patterns. This algorithm includes a novel multiresolution pruning technique. Finally, experimental results are provided to show the strength of the algorithm and design decisions related to performance tuning.

[1] Open GIS Consortium, Inc., http:/www.opengis.org, 2004.
[2] R. Agarwal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int'l Conf. Very Large Data Bases, 1994.
[3] L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. Vitter, “Scalable Sweeping-Based Spatial Join,” Proc. Int'l Conf.Very Large Databases, 1998.
[4] C. Berge, Graphs and Hypergraphs. American Elsevier, 1976.
[5] Y. Chou, Exploring Spatial Analysis in Geographic Information System. Onward Press, 1997.
[6] N.A.C. Cressie, Statistics for Spatial Data. Wiley and Sons, 1991.
[7] P. Dixon, “Ripley's $K$ Function,” http://www.stat.iastate.edu/preprint/articles 2001-18.pdf, Dec. 2001.
[8] V. Estivill-Castro and I. Lee, “Data Mining Techniques for Autonomous Exploration of Large Volumes of Geo-Referenced Crime Data,” Proc. Sixth Int'l Conf. Geocomputation, 2001.
[9] V. Estivill-Castro and A. Murray, “Discovering Associations in Spatial Data— An Efficient Medoid Based Approach,” Proc. Second Pacific-Asia Conf. Knowledge Discovery and Data Mining, 1998.
[10] G. Graefe, “Sort-Merge-Join: An Idea Whose Time has (h) Passed?” Proc. IEEE Conf. Data Eng., 1994.
[11] D. J. DeWitt and J.M. Patel, “Partition Based Spatial-Merge Join,” Proc. ACM SIGMOD Conf. Management of Data, June 1996.
[12] K. Koperski and J. Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proc. Fourth Int'l Symp. Spatial Databases, 1995.
[13] S.T. Leutenegger and M.A. Lopez, “The Effect of Buffering on the Performance of R-Trees,” Proc. Int'l Conf. Data Eng., 1998.
[14] S. Lipschutz, Schaum's Outline of General Topology. McGraw-Hill Trade, 1968.
[15] Y. Morimoto, “Mining Frequent Neighboring Class Sets in Spatial Databases,” Proc. ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, 2001.
[16] B.D. Ripley, “The Second-Order Analysis of Stationary Point Process,” J. Applied Probability, vol. 13, pp. 255-266, 1976.
[17] B.D. Ripley, “Modelling Spatial Patterns,” J. Royal Statistical Soc. Series B, vol. 39, pp. 172-192, 1977.
[18] S. Shekhar and S. Chawla, Spatial Databases: A Tour. Prentice Hall, 2003.
[19] S. Shekhar and Y. Huang, “Colocation Rules Mining: A Summary of Results,” Proc. Seventh Int'l Symp. Spatio-Temporal Databases, 2001.
[20] P. Tan, M. Steinbac, V. Kumar, C. Potter, and S. Klooster, “Finding Spatio-Temporal Patterns in Earth Science Data,” Proc. KDD Workshop Temporal Data Mining, 2001.

Index Terms:
Colocation patterns, spatial association rules, participation index.
Citation:
Yan Huang, Shashi Shekhar, Hui Xiong, "Discovering Colocation Patterns from Spatial Data Sets: A General Approach," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1472-1485, Dec. 2004, doi:10.1109/TKDE.2004.90
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