Linial N, London E, Rabinovich Y. The Geometry of Graphs and Some of Its Algorithmic Applications. Combinatorica, 1995, 15(2): 215-245
[2]
Dasgupta S, Gupta A. An Elementary Proof of a Theorem of Johnson and Lindenstrauss. Random Structures & Algorithms, 2003, 22(1): 60-65
[3]
Cormode G, Datar M, Indyk P, et al. Comparing Data Streams Using Hamming Norms (How to Zero in). IEEE Trans on Knowledge and Data Engineering, 2003, 15(3): 529-540
[4]
Indyk P. Stable Distributions, Pseudorandom Generators, Embeddings, and Data Stream Computation. Journal of the ACM, 2006, 53(3): 307-323
[5]
Indyk P, Koudas N, Muthukrishnan S. Identifying Representative Trends in Massive Time Series Data Sets Using Sketches //Proc of the 26th International Conference on Very Large Data Bases. Cairo, Egypt, 2000: 363-372
[6]
Fern X Z, Brodly C E. Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach //Proc of the 20th International Conference on Machine Learning. Washington, USA, 2003: 186-193
[7]
Keogh E, Kasetty S. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Mining and Knowledge Discovery, 2003, 7(4): 349-371
[8]
Guha S, Meyerson A, Mishra N, et al. Clustering Data Streams: Theory and Practice. IEEE Trans on Knowledge and Data Engineering, 2003, 15(3): 515-528
[9]
Aggarwal C C, Han Jiawei, Wang Jianyong, et al. A Framework for Clustering Evolving Data Streams //Proc of the 29th International Conference on Very Large Data Base. Berlin, Germany, 2003: 81-92
[10]
Charikar M, O'Callaghan L, Panigrahy R. Better Streaming Algorithms for Clustering Problems // Proc of the 35th Annual ACM Symposium on Theory of Computing. San Diego, USA, 2003: 30-39
[11]
Beringer J, Hullermeier E. Online Clustering of Parallel Data Streams. Data & Knowledge Engineering, 2006, 58(2): 180-204
[12]
Yeh M Y, Dai Biru, Chen M S. Clustering over Multiple Evolving Streams by Events and Correlations. IEEE Trans on Knowledge and Data Engineering, 2007, 19(10): 1349-1362
[13]
Johnson W B, Lindenstrauss J. Extensions of Lipschitz Mappings into a Hilbert Space. Contemporary Mathematics, 1984, 26(1): 189-206
[14]
Achlioptas D. Database-Friendly Random Projections //Proc of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. Santa Barbara, USA, 2001: 274-281
[15]
Gilbert A C, Kotidis Y, Muthukrishnan S, et al. One-Pass Wavelet Decompositions of Data Streams. IEEE Trans on Knowledge and Data Engineering, 2003, 15(3): 541-554
[16]
Thaper N, Guha S, Indyk P, et al. Dynamic Multidimensional Histograms //Proc of the ACM SIGMOD International Conference on Management of Data. Madison, USA, 2002: 428-439
[17]
Cormode G, Muthukrishnan S. An Improved Data Stream Summary: The Count-Min Sketch and Its Applications. Journal of Algorithms, 2005, 55(1): 58-75
[18]
Aggarwal C C, Yu P S. Data Streams: Models and Algorithms. Berlin, Germany: Springer, 2007
[19]
Palpanas T, Vlachos M, Keogh E, et al. Online Amnesic Approximation of Streaming Time Series //Proc of the 20th International Conference on Data Engineering. Boston, USA, 2004: 339-349
[20]
Zhao Yanchang, Zhang Shichao. Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis. IEEE Trans on Knowledge and Data Engineering, 2006, 18(2): 231-244
[21]
Bulut A, Singh A K. SWAT: Hierarchical Stream Summarization in Large Networks //Proc of the 19th International Conference on Data Engineering. Vienna, Austria, 2003: 303-314
[22]
Potamias M, Patroumpas K, Sellis T. Amnesic Online Synopses for Moving Objects //Proc of the 15th ACM International Conference on Information and Knowledge Management. Arlington, USA, 2006: 784-785
[23]
Yu P S, Wang Jianyong, Aggarwal C C, et al. A Framework for On-Demand Classification of Evolving Data Streams. IEEE Trans on Knowledge and Data Engineering, 2006, 18(5): 577-589
[24]
Lerman I, Costa J, Silva H. Validation of Very Large Data Sets Clustering by Means of a Nonparametric Linear Criterion // Tajuga K, Sokolowski A, Bock H H, eds. Classification, Clustering and Data Analysis: Recent Advances and Application. Berlin, Germany: Springer, 2002: 147-157
[25]
Gavrilov M, Anguelov D, Indyk P, et al. Mining the Stock Market: Which Measure Is Best? //Proc of the 6th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. Boston, USA, 2000: 487-496
[26]
Keogh E, Xi Xiaopeng, Wei Li, et al. The UCR Time Series Classification/Clustering Homepage [DB/OL]. [2007-08-01]. http:// www.cs.ucr.edu/~eamonn/time_series_data
