
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Christian S. Jensen, Dan Lin, Beng Chin Ooi, "Continuous Clustering of Moving Objects," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 9, pp. 11611174, September, 2007.  
BibTex  x  
@article{ 10.1109/TKDE.2007.1054, author = {Christian S. Jensen and Dan Lin and Beng Chin Ooi}, title = {Continuous Clustering of Moving Objects}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {19}, number = {9}, issn = {10414347}, year = {2007}, pages = {11611174}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.1054}, 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  Continuous Clustering of Moving Objects IS  9 SN  10414347 SP1161 EP1174 EPD  11611174 A1  Christian S. Jensen, A1  Dan Lin, A1  Beng Chin Ooi, PY  2007 KW  Spatial databases KW  Temporal databases KW  Clustering VL  19 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, “Automatic Subspace Clustering of High Dimensional Data for Data Mining Application,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '98), pp. 94105, 1998.
[2] M. Ankerst, M. Breunig, H.P. Kriegel, and J. Sander, “OPTICS: Ordering Points to Identify the Clustering Structure,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '99), pp. 4960, 1999.
[3] Applied Generics, RoDIN24, www.appliedgenerics.com/down loadsRoDIN24Brochure.pdf , 2006.
[4] J. Basch, L.J. Guibas, and J. Hershberger, “Data Structures for Mobile Data,” Algorithms, vol. 31, no. 1, pp. 128, 1999.
[5] C.S. Jensen, D. Tiesyte, and N. Tradisauskas, “The COST BenchmarkComparison and Evaluation of SpatioTemporal Indexes,” Proc. 11th Int'l Conf. Database Systems for Advanced Applications (DASFAA '06), pp. 125140, 2006.
[6] T.F. Gonzalez, “Clustering to Minimize the Maximum Intercluster Distance,” Theoretical Computer Science, vol. 38, pp. 293306, 1985.
[7] S. Guha, R. Rastogi, and K. Shim, “CURE: An Efficient Clustering Algorithm for Large Databases,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '98), pp. 7384, 1998.
[8] M. Hadjieleftheriou, G. Kollios, D. Gunopulos, and V.J. Tsotras, “OnLine Discovery of Dense Areas in SpatioTemporal Databases,” Proc. Eighth Int'l Symp. Spatial and Temporal Databases (SSTD '03), pp. 306324, 2003.
[9] S. HarPeled, “Clustering Motion,” Discrete and Computational Geometry, vol. 31, no. 4, pp. 545565, 2003.
[10] V.S. Iyengar, “On Detecting SpaceTime Clusters,” Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '04), pp. 587592, 2004.
[11] A.K. Jain, M.N. Murty, and P.J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264323, 1999.
[12] C.S. Jensen, D. Lin, and B.C. Ooi, “Query and Update Efficient ${\rm B}^{+}{\hbox{}}{\rm{Tree}}$ Based Indexing of Moving Objects,” Proc. 30th Int'l Conf. Very Large Data Bases (VLDB '04), pp. 768779, 2004.
[13] P. Kalnis, N. Mamoulis, and S. Bakiras, “On Discovering Moving Clusters in SpatioTemporal Data,” Proc. Ninth Int'l Symp. Spatial and Temporal Databases (SSTD '05), pp. 364381, 2005.
[14] G. Karypis, E.H. Han, and V. Kumar, “Chameleon: Hierarchical Clustering Algorithm Using Dynamic Modeling,” Computer, vol. 32, no. 8, pp. 6875, Aug. 1999.
[15] D. Kwon, S. Lee, and S. Lee, “Indexing the Current Positions of Moving Objects Using the Lazy Update RTree,” Proc. Third Int'l Conf. Mobile Data Management (MDM '02), pp. 113120, 2002.
[16] Y. Li, J. Han, and J. Yang, “Clustering Moving Objects,” Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '04), pp. 617622, 2004.
[17] J. Macqueen, “Some Methods for Classification and Analysis of Multivariate Observations,” Proc. Fifth Berkeley Symp. Math. Statistics and Probability, pp. 281297, 1967.
[18] S. Nassar, J. Sander, and C. Cheng, “Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '04), pp.467478, 2004.
[19] R. Ng and J. Han, “Efficient and Effective Clustering Method for Spatial Data Mining,” Proc. 20th Int'l Conf. Very Large Data Bases (VLDB '94), pp. 144155, 1994.
[20] J.M. Patel, Y. Chen, and V.P. Chakka, “STRIPES: An Efficient Index for Predicted Trajectories,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '04), pp. 637646, 2004.
[21] S. Šaltenis, C.S. Jensen, S.T. Leutenegger, and M.A. Lopez, “Indexing the Positions of Continuously Moving Objects,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '00), pp.331342, 2000.
[22] M. Spiliopoulou, I. Ntoutsi, Y. Theodoridis, and R. Schult, “MONIC: Modeling and Monitoring Cluster Transitions,” Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '06), pp. 706711, 2006.
[23] Y. Tao, C. Faloutsos, D. Papadias, and B. Liu, “Prediction and Indexing of Moving Objects with Unknown Motion Patterns,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '04), pp. 611622, 2004.
[24] Y. Tao, D. Papadias, and J. Sun, “The ${\rm TPR}^{\ast}{\hbox{}}{\rm{Tree}}$ : An Optimized SpatioTemporal Access Method for Predictive Queries,” Proc. 29th Int'l Conf. Very Large Data Bases (VLDB '03), pp. 790801, 2003.
[25] W. Wang, J. Yang, and R. Muntz, “Sting: A Statistical Information Grid Approach to Spatial Data Mining,” Proc. 23rd Int'l Conf. Very Large Data Bases (VLDB '97), pp. 186195, 1997.
[26] M.L. Yiu and N. Mamoulis, “Clustering Objects on a Spatial Network,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '04), pp. 443454, 2004.
[27] Q. Zhang and X. Lin, “Clustering Moving Objects for SpatioTemporal Selectivity Estimation,” Proc. 15th Australasian Database Conf. (ADC '04), pp. 123130, 2004.
[28] T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '96), pp. 103114, 1996.