
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Zhenjie Zhang, Yin Yang, Anthony K.H. Tung, Dimitris Papadias, "Continuous kMeans Monitoring over Moving Objects," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 9, pp. 12051216, September, 2008.  
BibTex  x  
@article{ 10.1109/TKDE.2008.54, author = {Zhenjie Zhang and Yin Yang and Anthony K.H. Tung and Dimitris Papadias}, title = {Continuous kMeans Monitoring over Moving Objects}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {20}, number = {9}, issn = {10414347}, year = {2008}, pages = {12051216}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.54}, 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 kMeans Monitoring over Moving Objects IS  9 SN  10414347 SP1205 EP1216 EPD  12051216 A1  Zhenjie Zhang, A1  Yin Yang, A1  Anthony K.H. Tung, A1  Dimitris Papadias, PY  2008 KW  Data mining KW  Spatial databases and GIS VL  20 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] D. Arthur and S. Vassilvitskii, “How Slow is the $k\hbox{}{\rm Means}$ Method,” Proc. 22nd ACM Symp. Computational Geometry (SoCG), 2006.
[2] B. Babcock, M. Datar, R. Motwani, and L. O'Callaghan, “Maintaining Variance and $k\hbox{}{\rm Means}$ over Data Stream Windows,” Proc. ACM Symp. Principles of Database Systems (PODS), 2003.
[3] P. Bradley and U. Fayyad, “Refining Initial Points for $k\hbox{}{\rm Means}$ Clustering,” Proc. 15th Int'l Conf. Machine Learning (ICML), 1998.
[4] T. Brinkhoff, “A Framework for Generating NetworkBased Moving Objects,” GeoInformatica, vol. 6, no. 2, pp. 153180, 2002.
[5] A. Datta, D. Vandermeer, A. Celik, and V. Kumar, “Broadcast Protocols to Support Efficient Retrieval from Databases by Mobile Users,” ACM Trans. Database Systems, vol. 24, no. 1, pp. 179, 1999.
[6] B. Gedik and L. Liu, “MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System,” Proc. Ninth Int'l Conf. Extending Database Technology (EDBT), 2004.
[7] S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O'Callaghan, “Clustering Data Streams: Theory and Practice,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 3, pp. 515528, May/June 2003.
[8] S. HarPeled and B. Sadri, “How Fast is the $k\hbox{}{\rm Means}$ Method,” Proc. 16th Ann. ACMSIAM Symp. Discrete Algorithms (SODA), 2005.
[9] H. Hu, J. Xu, and D. Lee, “A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects,” Proc. ACM SIGMOD, 2005.
[10] M. Inaba, N. Katoh, and H. Imai, “Applications of Weighted Voronoi Diagrams and Randomization to VarianceBased Clustering,” Proc. 10th ACM Symp. Computational Geometry (SoCG), 1994.
[11] A. Jain, M. Murty, and P. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264323, 1999.
[12] C. Jensen, D. Lin, and B.C. Ooi, “Continuous Clustering of Moving Objects,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 9, pp.11611173, Sept. 2007.
[13] C. Jensen, D. Lin, B.C. Ooi, and R. Zhang, “Effective Density Queries on Continuously Moving Objects,” Proc. 22nd IEEE Int'l Conf. Data Eng. (ICDE), 2006.
[14] P. Kalnis, N. Mamoulis, and S. Bakiras, “On Discovering Moving Clusters in SpatioTemporal Data,” Proc. Ninth Int'l Symp. Spatial and Temporal Databases (SSTD), 2005.
[15] J.M. Kang, M. Mokbel, S. Shekhar, T. Xia, and D. Zhang, “Continuous Evaluation of Monochromatic and Bichromatic Reverse Nearest Neighbors,” Proc. 23rd IEEE Int'l Conf. Data Eng. (ICDE), 2007.
[16] T. Kanungo, M. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu, “An Efficient $k\hbox{}{\rm Means}$ Clustering Algorithm: Analysis and Implementation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881892, July 2002.
[17] L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, 1990.
[18] A. Kumar, Y. Sabharwal, and S. Sen, “A Simple Linear Time $(1 + \varepsilon)\hbox{}{\rm Approximation}$ Algorithm for $k\hbox{}{\rm Means}$ Clustering in Any Dimensions,” Proc. 45th Ann. IEEE Symp. Foundations of Computer Science (FOCS), 2004.
[19] Y. Li, J. Han, and J. Yang, “Clustering Moving Objects,” Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2004.
[20] S. Lloyd, “Least Squares Quantization in PCM,” IEEE Trans. Information Theory, vol. 28, no. 2, pp. 129136, 1982.
[21] M. Meila, “The Uniqueness of a Good Optimum for $k\hbox{}{\rm Means}$ ,” Proc. 23rd Int'l Conf. Machine Learning (ICML), 2006.
[22] M. Mokbel, X. Xiong, and W. Aref, “SINA: Scalable Incremental Processing of Continuous Queries in SpatioTemporal Databases,” Proc. ACM SIGMOD, 2004.
[23] K. Mouratidis, M. Hadjieleftheriou, and D. Papadias, “Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring,” Proc. ACM SIGMOD, 2005.
[24] K. Mouratidis, D. Papadias, S. Bakiras, and Y. Tao, “A ThresholdBased Algorithm for Continuous Monitoring of $K$ Nearest Neighbors,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 11, pp. 14511464, Nov. 2005.
[25] K. Mouratidis, D. Papadias, and S. Papadimitriou, “TreeBased Partitioning Querying: A Methodology for Computing Medoids in Large Spatial Datasets,” The VLDB J., vol. 17, no. 4, pp. 923945, 2008.
[26] K. Mouratidis, M. Yiu, D. Papadias, and N. Mamoulis, “Continuous Nearest Neighbor Monitoring in Road Networks,” Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB), 2006.
[27] R. Ng and J. Han, “Efficient and Effective Clustering Method for Spatial Data Mining,” Proc. 20th Int'l Conf. Very Large Data Bases (VLDB), 1994.
[28] S. Papadopoulos, D. Sacharidis, and K. Mouratidis, “Continuous Medoid Queries over Moving Objects,” Proc. 10th Int'l Symp. Spatial and Temporal Databases (SSTD), 2007.
[29] D. Pelleg and A. Moore, “Accelerating Exact $k\hbox{}{\rm Means}$ Algorithms with Geometric Reasoning,” Proc. Fifth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 1999.
[30] T. Xia and D. Zhang, “Continuous Reverse Nearest Neighbor Monitoring,” Proc. 22nd IEEE Int'l Conf. Data Eng. (ICDE), 2006.
[31] X. Xiong, M. Mokbel, and W. Aref, “SEACNN: Scalable Processing of Continuous $K\hbox{}{\rm Nearest}$ Neighbor Queries in SpatioTemporal Databases,” Proc. 21st IEEE Int'l Conf. Data Eng. (ICDE), 2005.
[32] X. Yu, K. Pu, and N. Koudas, “Monitoring $K\hbox{}{\rm Nearest}$ Neighbor Queries over Moving Objects,” Proc. 21st IEEE Int'l Conf. Data Eng. (ICDE), 2005.
[33] D. Zhang, Y. Du, and L. Hu, “On Monitoring the ${\rm Top}\hbox{}k$ Unsafe Places,” Proc. 24th IEEE Int'l Conf. Data Eng. (ICDE), 2008.
[34] D. Zhang, Y. Du, T. Xia, and Y. Tao, “Progressive Computation of MinDist OptimalLocation Query,” Proc. 32nd Int'l Conf. Very Large Data Bases (VLDB), 2006.
[35] Z. Zhang, B. Dai, and A. Tung, “On the Lower Bound of Local Optimum in $k\hbox{}{\rm Means}$ Algorithm,” Proc. Sixth IEEE Int'l Conf. Data Mining (ICDM), 2006.