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Issue No.10 - October (2008 vol.20)
pp: 1322-1335
Mete Celik , University of Minnesota, Minneapolis
Shashi Shekhar , University of Minnesota, Minneapolis
James P. Rogers , U.S. Army ERDC, Alexandria
James A. Shine , U.S. Army ERDC, Alexandria
ABSTRACT
Mixed-drove spatio-temporal co-occurrence patterns (MDCOPs) represent subsets of two or more different object-types whose instances are often located in spatial and temporal proximity. Discovering MDCOPs is an important problem with many applications such as identifying tactics in battlefields, games, and predator-prey interactions. However, mining MDCOPs is computationally very expensive because the interest measures are computationally complex, datasets are larger due to the archival history, and the set of candidate patterns is exponential in the number of object-types. We propose a monotonic composite interest measure for discovering MDCOPs and novel MDCOP mining algorithms. Analytical results show that the proposed algorithms are correct and complete. Experimental results also show that the proposed methods are computationally more efficient than naive alternatives.
INDEX TERMS
Mining methods and algorithms, Data mining, Spatial databases and GIS
CITATION
Mete Celik, Shashi Shekhar, James P. Rogers, James A. Shine, "Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 10, pp. 1322-1335, October 2008, doi:10.1109/TKDE.2008.97
REFERENCES
[1] A. Brix and P.J. Diggle, “Spatio-Temporal Prediction for Log-Gaussian Cox Processes,” J. Royal Statistical Soc., vol. 63, no. 10, pp. 823-841, 2001.
[2] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int'l Conf. Very Large Data Bases (VLDB), 1994.
[3] S. Banerjee, B.P. Carlin, and A.E. Gelfrand, Hierarchical Modeling and Analysis for Spatial Data. CRC Press, 2003.
[4] H. Cao, N. Mamoulis, and D.W. Cheung, “Discovery of Collocation Episodes in Spatiotemporal Data,” Proc. Sixth IEEE Int'l Conf. Data Mining (ICDM '06), pp. 823-827, 2006.
[5] M. Celik, S. Shekhar, J.P. Rogers, J.A. Shine, and J.S. Yoo, “Mixed-Drove Spatio-Temporal Co-Occurrence Pattern Mining: A Summary of Results,” Proc. Sixth IEEE Int'l Conf. Data Mining (ICDM '06), pp. 119-1287, 2006.
[6] M. Celik, S. Shekhar, J.P. Rogers, and J.A. Shine, “Mixed-Drove Spatio-Temporal Co-Occurrence Pattern Mining,” technical report, Computer Science Dept., Univ. of Minnesota, pp. 08-015, 2008.
[7] N.A.C. Cressie, Statistics for Spatial Data. Wiley, 1993.
[8] C.W.C. Granger, “Time Series Analysis, Cointegration, and Applications,” Nobel Prize Lecture, Paper 2004-02, Dept. of Economics, Univ. of California, San Diego, 2004.
[9] J. Gudmundsson and M.v. Kreveld, “Computing Longest Duration Flocks in Trajectory Data,” Proc. 14th Ann. ACM Int'l Symp. Geographic Information Systems (ACM-GIS '06), pp. 35-42, 2006.
[10] J. Gudmundsson, M.v. Kreveld, and B. Speckmann, “Efficient Detection of Motion Patterns in Spatio-Temporal Data Sets,” Proc. 12th Ann. ACM Int'l Workshop Geographic Information Systems (ACM-GIS '04), pp. 250-257, 2004.
[11] R.H. Guting and M. Schneider, Moving Object Databases. Morgan Kaufmann, 2005.
[12] M. Hadjieleftheriou, G. Kollios, P. Bakalov, and V.J. Tsotras, “Complex Spatio-Temporal Pattern Queries,” Proc. 31st Int'l Conf. Very Large Data Bases (VLDB '05), pp. 877-888, 2005.
[13] Y. Huang, S. Shekhar, and H. Xiong, “Discovering Co-Location Patterns from Spatial Datasets: A General Approach,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 12, pp. 1472-1485, Dec. 2004.
[14] P. Kalnis, N. Mamoulis, and S. Bakiras, “On Discovering Moving Clusters in Spatio-Temporal Data,” Proc. Ninth Int'l Symp. Spatial and Temporal Databases (SSTD), 2005.
[15] M. Koubarakis, T.K. Sellis, A.U. Frank, S. Grumbach, R.H. Guting, C.S. Jensen, N.A. Lorentzos, Y. Manolopoulos, E. Nardelli, B. Pernici, H.J. Schek, M. Scholl, B. Theodoulidis, and N. Tryfona, “Spatio-Temporal Databases: The Chorochronos Approach,” Lecture Notes in Computer Science, vol. 2520, 2003.
[16] P. Laube and S. Imfeld, “Analyzing Relative Motion within Groups of Trackable Moving Point Objects,” Proc. Second Int'l Conf. Geographic Information Science (GIScience '02), pp. 132-144, 2002.
[17] P. Laube, M.v. Kreveld, and S. Imfeld, “Finding REMO— Detecting Relative Motion Patterns in Geospatial Lifelines,” Proc. 11th Int'l Symp. Spatial Data Handling (SDH '04), pp.201-214, 2004.
[18] J. Ma, D. Zeng, and H. Chen, “Spatial-Temporal Cross-Correlation Analysis: A New Measure and a Case Study in Infectious Disease Informatics,” Proc. IEEE Int'l Conf. Intelligence and Security Informatics (ISI '06), pp. 542-547, 2006.
[19] C. Mouza and P. Rigaux, “Mobility Patterns,” GeoInformatica, vol. 9, no. 4, pp. 297-319, 2005.
[20] B.D. Ripley, Spatial Statistics. Wiley, 1981.
[21] O. Schabenberger and C.A. Gotway, Statistical Methods for Spatial Data Analysis. Chapman and Hall, 2004.
[22] S. Shekhar, Y. Huang, and H. Xiong, “Discovering Spatial Co-Location Patterns: A Summary of Results,” Proc. Seventh Int'l Symp. Spatial and Temporal Databases (SSTD '01), pp. 236-256, 2001.
[23] Proc. First Int'l Workshop Spatial and Spatio-Temporal Data Mining (SSTDM '06), in conjunction with the Sixth IEEE Int'l Conf. Data Mining (ICDM), 2006.
[24] J. Wang, W. Hsu, and M.L. Lee, “A Framework for Mining Topological Patterns in Spatio-Temporal Databases,” Proc. ACM 14th Conf. Information and Knowledge Management (CIKM '05), pp. 429-436, 2005.
[25] W.W.S. Wei, Time Series Analysis: Univariate and Multivariate Methods. Addison Wesley, 2005.
[26] H. Yang, S. Parthasarathy, and S. Mehta, “A Generalized Framework for Mining Spatio-Temporal Patterns in Scientific Data,” Proc. 11th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '05), pp. 716-721, 2005.
[27] J.S. Yoo and S. Shekhar, “A Partial Join Approach for Mining Co-Location Patterns,” Proc. 12th Ann. ACM Int'l Workshop Geographic Information Systems (ACM-GIS), 2005.
[28] J.S. Yoo and S. Shekhar, “A Joinless Approach for Mining Spatial Colocation Patterns,” IEEE Trans. Knowledge and Data Eng., vol. 18, no. 10, Oct. 2006.
[29] J.S. Yoo, S. Shekhar, and M. Celik, “A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results,” Proc. Fifth IEEE Int'l Conf. Data Mining (ICDM), 2005.
[30] X. Zhang, N. Mamoulis, D.W. Cheung, and Y. Shou, “Fast Mining of Spatial Collocations,” Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '04), pp. 384-393, 2004.
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