
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu, "A Framework for OnDemand Classification of Evolving Data Streams," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 577589, May, 2006.  
BibTex  x  
@article{ 10.1109/TKDE.2006.69, author = {Charu C. Aggarwal and Jiawei Han and Jianyong Wang and Philip S. Yu}, title = {A Framework for OnDemand Classification of Evolving Data Streams}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {18}, number = {5}, issn = {10414347}, year = {2006}, pages = {577589}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.69}, 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  A Framework for OnDemand Classification of Evolving Data Streams IS  5 SN  10414347 SP577 EP589 EPD  577589 A1  Charu C. Aggarwal, A1  Jiawei Han, A1  Jianyong Wang, A1  Philip S. Yu, PY  2006 KW  Stream classification KW  geometric time frame KW  microclustering KW  nearest neighbor. VL  18 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] C.C. Aggarwal, J. Han, J. Wang, and P. Yu, “On Demand Classification of Data Streamsm,” Proc. ACM KDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 503508, Aug. 2004.
[2] C.C. Aggarwal, J. Han, J. Wang, and P. Yu, “CluStream: A Framework for Clustering Evolving Data Streams,” Proc. Int'l Conf. Very Large Data Bases, pp. 8192, Sept. 2003.
[3] C.C. Aggarwal, “A Framework for Diagnosing Changes in Evolving Data Streams,” Proc. ACM SIGMOD Conf., pp. 575586, June 2003.
[4] B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom, “Models and Issues in Data Stream Systems,” Proc. 21st ACM SIGACTSIGMODSIGART Symp. Principles of Database Systems, pp. 116, June 2002.
[5] L. O'Callaghan, N. Mishra, A. Meyerson, S. Guha, and R. Motwani, “StreamingData Algorithms For HighQuality Clustering,” Proc. 18th Int'l Conf. Data Eng., pp. 685696, Feb. 2002.
[6] P. Bradley, U. Fayyad, and C. Reina, “Scaling Clustering Algorithms to Large Databases,” Proc. Knowledge Discovery and Data Mining Conf., pp. 915, 1998.
[7] Y. Chen, G. Dong, J. Han, B.W. Wah, and J. Wang, “MultiDimensional Regression Analysis of TimeSeries Data Streams,” Proc. 28th Int'l Conf. Very Large Data Bases, pp. 323334, Aug. 2002.
[8] P. Domingos and G. Hulten, “Mining HighSpeed Data Streams,” Proc. Sixth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 7180, Aug. 2000.
[9] P. Domingos and G. Hulten, “A General Method for Scaling Up Machine Learning Algorithms and Its Application to Clustering,” Proc. Int'l Conf. Machine Learning, pp. 106113, 2001.
[10] R. Duda and P. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
[11] J.H. Friedman, “A Recursive Partitioning Decision Rule for NonParametric Classifiers,” IEEE Trans. Computers, vol. 26, pp. 404408, 1977.
[12] J. Gehrke, V. Ganti, R. Ramakrishnan, and W.Y. Loh, “BOAT: Optimistic Decision Tree Construction,” Proc. 1999 ACM SIGMOD Int'l Conf. Management of Data, pp. 169180, June 1999.
[13] G. Hulten, L. Spencer, and P. Domingos, “Mining Time Changing Data Streams,” Proc. Seventh ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 97106, Aug. 2001.
[14] F. Farnstrom, J. Lewis, and C. Elkan, “Scalability for Clustering Algorithms Revisited,” SIGKDD Explorations, vol. 2, no. 1, pp. 5157, 2000.
[15] J. Feigenbaum, S. Kannan, M. Strauss, and M. Viswanathan, “Testing and SpotChecking of Data Streams,” Proc. 11th Ann. ACMSIAM Symp. Discrete Algorithms, pp. 165174, Jan. 2000.
[16] F.J. FerrerTroyano, J.S. AguilarRuiz, and J.C. Riquelme, “Discovering Decision Rules from Numerical Data Streams,” ACM Symp. Applied Computing, pp. 649653, 2004.
[17] J. Fong and M. Strauss, “An Approximate $L^p{\hbox{}}{\rm{Difference}}$ Algorithm for Massive Data Streams,” Proc. 17th Ann. Symp. Theoretical Aspects of Computer Science, pp. 193204, Feb. 2000.
[18] J. Gama, R. Rocha, and P. Medas, “Accurate Decision Trees for Mining HighSpeed Data Streams,” Proc. Ninth Int'l Conf. Knowledge Discovery and Data Mining, pp. 523528, Aug. 2003.
[19] J. Gehrke, F. Korn, and D. Srivastava, “On Computing Correlated Aggregates over Continual Data Streams,” Proc. 2001 ACM SIGMOD Int'l Conf. Management of Data, pp. 271282, May 2001.
[20] S. Guha and N. Koudas, “Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation,” Proc. 18th Int'l Conf. Data Eng., pp. 567578, Feb. 2002.
[21] S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan, “Clustering Data Streams,” Proc. 41st Annual Symp. Foundations of Computer Science, pp. 359366, Nov. 2000.
[22] A. Gilbert, Y. Kotidis, S. Muthukrishnan, and M. Strauss, “Surfing Wavelets on Streams: OnePass Summaries for Approximate Aggregate Queries,” Proc. 27th Int'l Conf. Very Large Data Bases, pp. 7988, Sept. 2001.
[23] R. Jin and G. Agrawal, “Efficient Decision Tree Construction on Streaming Data,” Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 571576, Aug. 2003.
[24] R. Motwani, J. Widom, A. Arasu, B. Babcock, S. Babu, M. Datar, G. Manku, C. Olston, J. Rosenstein, and R. Varma, “Query Processing, Resource Management, and Approximation in a Data Stream Management System,” Proc. First Biennial Conf. Innovative Data Systems Research, Jan. 2003.
[25] H. Wang, W. Fan, P. Yu, and J. Han, “Mining ConceptDrifting Data Streams Using Ensemble Classifiers,” Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 226235, Aug. 2003.
[26] T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: An Efficient Data Clustering Method for Very Large Databases,” Proc. 1996 ACM SIGMOD Int'l Conf. Management of Data, pp. 103114, June 1996.