Mining Data Streams Bibliography

Maintained by: Mohamed Medhat Gaber

If you would like to submit any related paper to be added to this bibliography, please send an email to:

mohamed.m.gaber (-at-) gmail.com

Researchers in Data Stream Mining

[ACC03] D. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, C. Erwin, E. Galvez, M. Hatoun, J. Hwang, A. Maskey, A. Rasin, A. Singer, M. Stonebraker, N. Tatbul, Y. Xing, R.Yan, S. Zdonik. Aurora: A Data Stream Management System (Demonstration). In proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'03), San Diego, CA, June 2003.

[Abo96] G. Abowd, Ubiquitous Computing: Research themes and open issues from an applications perspective. Technical Report GIT-GVU 96-23, GVU Center, Georgia Institute of Technology, October 1996.

[ABB03] A. Arasu, B. Babcock. S. Babu, M. Datar, K. Ito, I. Nishizawa, J. Rosenstein, and J. Widom. STREAM: The Stanford Stream Data Manager Demonstration description - short overview of system status and plans; in Proc. of the ACM Intl Conf. on Management of Data (SIGMOD 2003), June 2003.

[Agg07] C. Aggarwal, BOOK: Data Streams: Models and Algorithms, Ed. Charu Aggarwal, Springer, 2007.

[Agg02] C. Aggarwal, An Intuitive Framework for Understanding Changes in Evolving Data Streams, Proceedings of the ICDE Conference, 2002.

[Agg03] C. Aggarwal, A Framework for Diagnosing Changes in Evolving Data Streams, Proceedings of the ACM SIGMOD Conference, 2003.

[AHW03] C. Aggarwal, J. Han, J. Wang, P. S. Yu,  A Framework for Clustering Evolving Data Streams,  Proc.  2003 Int. Conf. on Very Large Data Bases (VLDB'03), Berlin, Germany, Sept. 2003.

[AHW04a] C. Aggarwal, J. Han, J. Wang, and P. S. Yu, A Framework for Projected Clustering of High Dimensional Data Streams, Proc. 2004 Int. Conf. on Very Large Data Bases (VLDB'04), Toronto, Canada, Aug. 2004.

[AHW04b] C. Aggarwal, J. Han, J. Wang, and P. S. Yu, On Demand Classification of Data Streams, Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04), Seattle, WA, Aug. 2004.

[AJS02] M. Ajtai, T.S. Jayram, R. Kumar, and D. Sivakumar. Approximate counting of inversions in a data stream. In 34th ACM Symposium on Theory of Computing, Montral, Qubec, Canada, 2002.

[BBD02] B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom. Models and issues in data stream systems. In Proceedings of PODS, 2002.

[BDM03a] B. Babcock, M. Datar, and R. Motwani. Load Shedding Techniques for Data Stream Systems (short paper)  In Proc. of the 2003 Workshop on Management and Processing of Data Streams (MPDS 2003), June 2003

[BDM03b] B. Babcock, M. Datar, R. Motwani, L. O'Callaghan: Maintaining Variance and k-Medians over Data Stream Windows, to appear in Proceedings of the 22nd Symposium on Principles of Database Systems (PODS 2003).

[BeH05] J. Beringer and E. Hullermeier, Online Clustering of Parallel Data Streams, Data & Knowledge Engineering, 2005.  

[BFR99] M. Burl, Ch. Fowlkes, J. Roden, A. Stechert, and S. Mukhtar, Diamond Eye: A distributed architecture for image data mining,  in SPIE DMKD, Orlando, April 1999.

[BGK04] Ben-David, S, Johannes Gehrke and Daniel Kifer, Detecting Change in Data Streams, Proceedings of VLDB 2004.

[BKP03] R. Bhargava, H. Kargupta, and M. Powers, Energy Consumption in Data Analysis for On-board and Distributed Applications, Proceedings of the ICML'03 workshop on Machine Learning Technologies for Autonomous Space Applications, 2003.

[CCF02] M. Charikar, K. Chen and M. Farach-Colton. Finding Frequent Items in Data Streams. International Colloquium on Automata,Languages, and Programming (ICALP '02) 508--515.

[CCP03] M. Charikar, L. O'Callaghan, and R. Panigrahy. Better streaming algorithms for clustering problems In Proc. of 35th ACM Symposium on Theory of Computing (STOC), 2003.

[CCP04] Y. D. Cai, D. Clutter, G. Pape, J. Han, M. Welge, and L. Auvil, MAIDS: Mining Alarming Incidents from Data Streams, (system demonstration), Proc. 2004 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'04), Paris, France, June 2004.

[CDH02] Y. Chen, G. Dong, J. Han, B. W. Wah, and J. Wang. Multi-Dimensional Regression Analysis of Time-Series Data Streams In VLDB Conference, 2002.

[CEZ06] F Cao, M Ester, W Qian, and A Zhou, Density-based Clustering over an Evolving Data Stream with Noise, To appear in Proceedings of the 2006 SIAM Conference on Data Mining (SDM'2006).

[ChZ04] F.Chu and C.Zaniolo, Fast and light boosting for adaptive mining of data streams, in Proc. of the 5th Pacific-Asic Conference on Knowledge Discovery and Data Mining (PAKDD), Sydney, May 2004.

[CMM02] Liadan O'Callaghan, Nina Mishra, Adam Meyerson, Sudipto Guha, and Rajeev Motwani. Streaming-data algorithms for high-quality clustering. Proceedings of IEEE International Conference on Data Engineering, March 2002.

[CoM03] G. Cormode, S. Muthukrishnan What's hot and what's not: tracking most frequent items dynamically. PODS 2003: 296-306

[CoM04] G. Cormode and S. Muthukrishnan., What is new: Finding significant differences in network data streams, INFOCOM 2004.

[CRA04] L. Chen, K. Reddy, and G. Agrawal, GATES: A Grid-Based Middleware for Processing Distributed Data Streams, in proceedings of Conference on High Performance Distributed Computing (HPDC), 2004.

[CYW05] Yun Chi, Philip S. Yu, Haixun Wang, Richard R. Muntz, Loadstar: A Load Shedding Scheme for Classifying Data Streams, The 2005 SIAM International Conference on Data Mining (SIAM SDM'05), 2005.

[DCP04] Y. Dora Cai, D. Clutter, G. Pape, J. Han, M. Welge, L. Auvil. MAIDS: Mining Alarming Incidents from Data Streams. Proceedings of the 23rd ACM SIGMOD (International Conference on Management of Data), June 13-18, 2004, Paris, France.

[DDP02] Q. Ding, Q. Ding, and W. Perrizo, Decision Tree Classification of Spatial Data Streams Using Peano Count Trees, Proceedings of the ACM 124 Symposium on Applied Computing, Madrid, Spain, March 2002, pp. 413417.

[DGI02] Mayur Datar, Aristides Gionis, Piotr Indyk, Rajeev Motwani: Maintaining Stream Statistics Over Sliding Windows (Extended Abstract) in Proceedings of 13th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2002).

[DGR03] Abhinandan Das, Johannes Gehrke and Mirek Riedewald, Approximate Join Processing Over Data Streams, Proc. of the 2003 ACM SIGMOD International Conference on Management of Data, 2003.

[DoH00] P. Domingos and G. Hulten. Mining High-Speed Data Streams. In Proceedings of the Association for Computing Machinery Sixth International Conference on Knowledge Discovery and Data Mining, pages 71--80, 2000.

[DoH01] P. Domingos and G. Hulten, A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering, Proceedings of the Eighteenth International Conference on Machine Learning, 2001, 106--113, Williamstown, MA, Morgan Kaufmann.

[DHL03] G. Dong, J. Han, L.V.S. Lakshmanan, J. Pei, H. Wang and P.S. Yu.  Online mining of changes from data streams: Research problems and preliminary results,  In Proceedings of the 2003 ACM SIGMOD Workshop on Management and Processing of Data Streams. In cooperation with the 2003 ACM-SIGMOD International Conference on Management of Data (SIGMOD'03), San Diego, CA, June 8, 2003.

[Fan04a] W Fan, StreamMiner: A Classifier Ensemble-based Engine to Mine Concept Drifting Data Streams, VLDB'2004

[Fan04b] Wei Fan, Systematic data selection to mine concept-drifting data streams. KDD 2004: 128-137.  

[FAR04] F.J. Ferrer-Troyano, J.S. Aguilar-Ruiz and J.C. Riquelme, Discovering Decision Rules from Numerical Data Streams, ACM Symposium on Applied Computing - SAC04, 2004 (ACM Press, pp. 649-653)

[FAR05] F.J. Ferrer-Troyano, J.S. Aguilar-Ruiz and J.C. Riquelme, Incremental Rule Learning based on Example Nearness from Numerical Data Streams, ACM Symposium on Applied Computing - SAC05, 2005 (ACM Press, pp. 568-572)

[FHW04] Wei Fan, Yi-an Huang, Haixun Wang, and Philip S. Yu, Active Mining of Data Streams, Proceedings of SIAM International Conference on Data Mining 2004.

[FHY04] Wei Fan, Yi-an Huang, Philip S. Yu, Decision Tree Evolution Using Limited Number of Labeled Data Items from Drifting Data Streams. ICDM 2004: 379-382

[GFH07a] J. Gao, W. Fan, and J. Han, On Appropriate Assumptions to Mine Data Streams: Analysis and Practice, 2007 IEEE International Conference on Data Mining (ICDM'07), Omaha, NE, Oct 2007.

[GFH07b] J. Gao, W. Fan, J. Han, and P. S. Yu, A General Framework for Mining Concept-Drifting Streams with Skewed Distribution, 2007 SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, April 2007.

[GGR02a] V. Ganti, Johannes Gehrke, Raghu Ramakrishnan: Mining Data Streams under Block Evolution. SIGKDD Explorations 3(2): 1-10 (2002).

[GGR02b] M. Garofalakis, Johannes Gehrke, Rajeev Rastogi: Querying and mining data streams: you only get one look a tutorial. SIGMOD Conference 2002: 635

[GHP03] C. Giannella, J. Han, J. Pei, X. Yan, and P.S. Yu, Mining Frequent Patterns in Data Streams at Multiple Time Granularities, in H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha (eds.), Next Generation Data Mining, AAAI/MIT, 2003.

[GhP04] Amol Ghoting and Srinivasan Parthasarathy, Facilitating Interactive Distributed Data Stream Processing and Mining, In Proceedings of the IEEE International Symposium on Parallel and Distributed Processing Systems (IPDPS), April 2004.

[GKM03] Anna C. Gilbert, Yannis Kotidis, S. Muthukrishnan, Martin Strauss: One-Pass Wavelet Decompositions of Data Streams. TKDE 15(3): 541-554 (2003)

[GKZ03] Gaber, M, M., Krishnaswamy, S., and Zaslavsky, A., Adaptive Mining Techniques for Data Streams Using Algorithm Output Granularity, The Australasian Data Mining Workshop (AusDM 2003), Held in conjunction with the 2003 Congress on Evolutionary Computation (CEC 2003), December, Canberra, Australia, Springer Verlag, Lecture Notes in Computer Science (LNCS).

[GKZ04a] Gaber, M. M., Krishnaswamy, S. and Zaslavsky, A. (2004). Cost-Efficient Mining Techniques for Data Streams. In Proc. Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand. CRPIT, 32. Purvis, M., Ed. ACS.

[GKZ04b] Gaber, M, M., Krishnaswamy, S., and Zaslavsky, A., (2004), A Wireless Data Stream Mining Model, Accepted for publication in the Third International Workshop on Wireless Information Systems (WIS 2004), Held in conjunction with the Sixth International Conference on Enterprise Information Systems (ICEIS 2004), Porto, Portugal, April 13-14, ICEIS Press, ISBN.

[GKZ04c] Gaber, M, M., Krishnaswamy, S., and Zaslavsky, A., Ubiquitous Data Stream Mining, Current Research and Future Directions Workshop Proceedings held in conjunction with The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia May 26 2004.

[GKZ05] Gaber, M, M., Krishnaswamy, S., and Zaslavsky, A., (2005), On-board Mining of Data Streams in Sensor Networks, Accepted as a chapter in the forthcoming book Advanced Methods of Knowledge Discovery from Complex Data, (Eds.) Sanghamitra Badhyopadhyay, Ujjwal Maulik, Lawrence Holder and Diane Cook, Springer Verlag.  

[GMM00] S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams. In Proceedings of the Annual Symposium on Foundations of Computer Science. IEEE, November 2000.

[GMM03] Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan, Clustering Data Streams: Theory and Practice TKDE special issue on clustering, vol. 15, 2003.

[GMR04] J. Gama and P. Medas and R. Rocha, Forest Trees for On-line Data, ACM Symposium on Applied Computing - SAC04, 2004.

[GMR05] J. Gama and P. Medas and P. Rodrigues, Learning Decision Trees from Dynamic Data Streams, ACM Symposium on Applied Computing - SAC05, 2005.

[GoO03] Lukasz Golab and M. Tamer Ozsu. Issues in Data Stream Management. In SIGMOD Record, Volume 32, Number 2, June 2003, pp. 5--14.

[GRM03] J. Gama, R. Rocha and P. Medas, Accurate Decision Trees for Mining High-Speed Data Streams, Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining, Edited by P.Domingos and C. Faloutsos, ACM Press, 2003.

[GZK04a] Gaber, M, M., Zaslavsky, A., and Krishnaswamy, S., (2004), A Cost-Efficient Model for Ubiquitous Data Stream Mining, in the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Perugia Italy, July 4-9.

[GZK04b] Gaber, M, M., Zaslavsky, A., and Krishnaswamy, S., Towards an Adaptive Approach for Mining Data Streams in Resource Constrained Environments, in the Proceedings of Sixth International Conference on Data Warehousing and Knowledge Discovery - Industry Track, Zaragoza, Spain, 30 August - 3 September, Lecture Notes in Computer Science (LNCS), Springer Verlag.

[GZK04c] Gaber, M, M., Zaslavsky, A., and Krishnaswamy, S., Resource-Aware Knowledge Discovery in Data Streams, Accepted for publication in the Proceedings of First International Workshop on Knowledge Discovery in Data Streams, to be held in conjunction with the 15th European Conference on Machine Learning (ECML 2004) and the 8th European Conference on the Principals and Practice of Knowledge Discovery in Databases (PKDD 2004), Pisa, Italy, 20-24 September 2004.

[HeC08] H. He and S. Chen, IMORL: Incremental Multiple Objects Recognition and Localization, IEEE Trans. Neural Networks, 2008

[HLW07] Xuegang Hu, Peipei Li, Xindong Wu, and Gongqing Wu, A Semi-Random Multiple Decision-Tree Algorithm for Mining Data Streams, Journal of Computer Science and Technology, 22(2007), 5: 711-724.

[HRR98] M. Henzinger, P. Raghavan and S. Rajagopalan, Computing on data streams , Technical Note 1998-011, Digital Systems Research Center, Palo Alto, CA, May 1998

[Hsu02] J. Hsu, Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century.  In D Colton, M J Payne, N Bhatnagar, and C R Woratschek (Eds.), The Proceedings of ISECON 2002, v 19 (San Antonio): 224b.  AITP Foundation for Information Technology Education. ISSN: 1542-7382.

[HSD01] G. Hulten, L. Spencer, and P. Domingos. Mining Time-Changing Data Streams. ACM SIGKDD 2001.

[HXD04] Z. He, X. Xu, S. Deng and J. Z. Huang. Clustering Categorical Data Streams, Journal of Computational Methods in Science and Engineering (JCMSE), 2004, to appear.

[IsS00] Carsten Isert and Karsten Schwan. ACDS: Adapting Computational Data Streams for high performance. In International Parallel and Distributed Processing Symposium 2000

[JaS05] S. Jaroszewicz and T. Scheffer, Fast Discovery of Unexpected Patterns in Data, Relative to a Bayesian Network, Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005.

[JiA03] R. Jin and G. Agrawal, Efficient Decision Tree Construction on Streaming Data, in proceedings of ACM SIGKDD 2003.

[JQS03] Cheqing Jin, Weining Qian, Chaofeng Sha, Jeffrey X. Yu, and Aoying Zhou. Dynamically Maintaining Frequent Items over a Data Stream. In Proceedings of the 12th ACM Conference on Information and Knowledge Management (CIKM’2003).

[KTV06] I. Katakis, G. Tsoumakas, I. Vlahavas, Dynamic Feature Space and Incremental Feature Selection for the Classification of Textual Data Streams, ECML/PKDD-2006 International Workshop on Knowledge Discovery from Data Streams, Berlin, Germany, 2006.

[KBL04] Hillol Kargupta, Ruchita Bhargava, Kun Liu, Michael Powers, Patrick Blair, Samuel Bushra, James Dull, Kakali Sarkar, Martin Klein, Mitesh Vasa, and David Handy, VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring, Proceedings of SIAM International Conference on Data Mining 2004.

[KCC03] S. Krishnamurthy, S. Chandrasekaran, O. Cooper, A. Deshpande, M. Franklin, J. Hellerstein, W. Hong, S. Madden, V. Raman, F. Reiss, and M. Shah. TelegraphCQ: An Architectural Status Report. IEEE Data Engineering Bulletin, Vol 26(1), March 2003.

[KCH01] Keogh, E., Chu, S., Hart, D. & Pazzani, M. (2001). An Online Algorithm for Segmenting Time Series. In Proceedings of IEEE International Conference on Data Mining. pp 289-296

[KDP02] M. Khan, Q. Ding, and W. Perrizo, K-nearest Neighbor Classification on Spatial Data Stream Using P-trees, Proceedings of the PAKDD, Taipei, Taiwan, May 2002, pp. 517-528. 128

[Kle02] J. Kleinberg, Bursty and Hierarchical Structure in Streams, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, Edmonton, Alberta, Canada, 2002.

[KLT03] E. Keogh, J. Lin, and W. Truppel. Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. In proceedings of the 3rd IEEE International Conference on Data Mining. Melbourne, FL. Nov 19-22, 2003.

[KLZ02] S. Krishnaswamy, S. Loke, and A. Zaslavsky, Towards Anytime Anywhere Data Mining E-Services, Proceedings of the Australian Data Mining Workshop (ADM'02) at the 15th Australian Joint Conference on Artificial Intelligence, (eds) S.J. Simoff, G.J. Williams, and M. Hegland. Canberra, Australia, December 2002, pp. 47 - 56, Published by the University of Technology Sydney, 2002. ISBN 0-9750075-0-5

[KoS03a] Nick Koudas and Divesh Srivastava. Data Stream Query Processing: A Tutorial. Presented at International Conference on Very Large Databases (VLDB), 2003.

[KoS03b] Christoph Koch, Stefanie Scherzinger: Attribute Grammars for Scalable Query Processing on XML Streams, Database Programming Languages, 9th International Workshop, DBPL 2003, Potsdam, Germany, September 6-8, 2003.

[KPP02] Kargupta, H., Park, B., Pittie, S., Liu, L., Kushraj, D. and Sarkar, K. (2002). MobiMine: Monitoring the Stock Market from a PDA. ACM SIGKDD Explorations. January 2002. Volume 3, Issue 2. Pages 37--46. ACM Press.

[KSS04a] Christoph Koch, Stefanie Scherzinger, Nicole Schweikardt, Bernhard Stegmaier: Schema-based Scheduling of Event Processors and Buffer Minimization for Queries on Structured Data Streams. Proceedings of VLDB 2004.

[KSS04b] Christoph Koch, Stefanie Scherzinger, Nicole Schweikardt, Bernhard Stegmaier: FluXQuery: An Optimizing XQuery Processor for Streaming XML Data. Proceedings of VLDB 2004.

[LAL01] Laerhoven, K. Van, Aidoo K., Lowette S., 2001. Real-time Analysis of Data from Many Sensors with Neural Networks. Proceedings of the fourth International Symposium on Wearable Computers (ISWC) Zurich, 7-9 October 2001. IEEE Press.

[Las02] M. Last, Online Classification of Nonstationary Data Streams, Intelligent Data Analysis, Vol. 6, No. 2, pp. 129-147, 2002.

[LKL03] J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. San Diego, CA. June 13, 2003.

[LLS04] Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan. An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams, Accepted for publication in the Proceedings of First International Workshop on Knowledge Discovery in Data Streams, to be held in conjunction with the 15th European Conference on Machine Learning (ECML 2004) and the 8th European Conference on the Principals and Practice of Knowledge Discovery in Databases (PKDD 2004), Pisa, Italy, 20-24 September 2004.

[LLS05] Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, DSM-TKP: Mining Top-K Path Traversal Patterns over Web Click-Streams, in Proc. of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), September 19-22, 2005.

[MaM02] G. S. Manku and R. Motwani. Approximate frequency counts over data streams. In Proceedings of the 28th International Conference on Very Large Data Bases, Hong Kong, China, August 2002.

[MLZ04] Martin H.C. Law, Nan Zhang, and Anil Jain, Nonlinear Manifold Learning for Data Stream, Proceedings of SIAM International Conference on Data Mining 2004.

[Mut03] S. Muthukrishnan (2003), Data streams: Algorithms and Applications. Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms.

[MWA03] Rajeev Motwani, Jennifer Widom, Arvind Arasu, Brian Babcock, Shivnath Babu, Mayur Datar, Gurmeet Manku, Chris Olston, Justin Rosenstein, and Rohit Varma, Query Processing, Approximation, and Resource Management in a Data Stream Management System,  Proceedings of the 2003 Conference on Innovative Data Systems Research.

[NCR03a] O. Nasraoui, C. Cardona, C. Rojas, F. González, TECNO-STREAMS: Tracking Evolving Clusters in Noisy Data Streams with a Scalable Immune System Learning Model, in Proc. of Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, FL, November 2003, pp. 235-242.

[NCR03b] Nasraoui O., Cardona C., Rojas C., and Gonzalez F., Mining Evolving User Profiles in Noisy Web Clickstream Data with a Scalable Immune System Clustering Algorithm, in Proc. of WebKDD 2003 – KDD Workshop on Web mining as a Premise to Effective and Intelligent Web Applications, Washington DC, August 2003, p. 71

[NRC04] O. Nasraoui, C. Rojas, and C. Cardona. Single Pass Mining of Evolving Trends in Web Data with Explicit Retrieval Similarity Measures. In Proceedings of “International Web Dynamics Workshop”, International World Wide Web Conference, New York, NY, May. 2004.

[OJW03] C. Olston, J. Jiang, and J. Widom. Adaptive Filters for Continuous Queries over Distributed Data Streams. ACM SIGMOD 2003 International Conference on Management of Data, San Diego, California, June 2003, pp. 563-574.

[Ord03] Carlos Ordonez. Clustering Binary Data Streams with K-means ACM DMKD 2003.

[OZN04] Kok-Leong Ong, Zili Zhang, Wee-Keong Ng, and Ee-Peng Lim. Agents and Stream Data Mining: A New Perspective. IEEE Intelligent Systems, to appear.

[PaK02] B. Park and H. Kargupta. Distributed Data Mining: Algorithms, Systems, and Applications. To be published in the Data Mining Handbook. Editor: Nong Ye. 2002.

[PFB03] S. Papadimitriou, C. Faloutsos, and A. Brockwell, Adaptive, Hands-Off Stream Mining, 29th International Conference on Very Large Data Bases VLDB, 2003.

[PHu01] P. Domingos and G. Hulten. Catching Up with the Data: Research Issues in Mining Data Streams. Workshop on Research Issues in Data Mining and Knowledge Discovery, 2001. Santa Barbara, CA

[POS04] Byung-Hoon Park, George Ostrouchov, Nagiza F. Samatova, and Al Geist, Reservoir-Based Random Sampling with Replacement from Data Stream, Proceedings of SIAM International Conference on Data Mining 2004.

[PRK01] S. Pirttikangas, J. Riekki, J. Kaartinen, J. Miettinen, S. Nissila, & J. Roning. Genie Of The Net: A New Approach For A Context-Aware Health Club. In Proceedings of Joint 12th ECML'01 and 5th European Conference on PKDD'01. September 3-7, 2001, Freiburg, Germany.

[PVK04] T. Palpanas, M. Vlachos, E. Keogh, D. Gunopulos, W. Truppel (2004). Online Amnesic Approximation of Streaming Time Series. In ICDE . Boston, MA, USA, March 2004.

[QQZ05] S Qin, W Qian, A Zhou, Adaptively Detecting Aggregation Bursts in Data Streams, In Proceedings of the 10th International Conference on Database Systems for Advanced Applications (DASFAA'2005).

[QQZ06] S Qin, W Qian, and A Zhou, Approximately Processing Multi-granularity Aggregate Queries over a Data Stream, To appear in Proceedings of the 22nd International Conference on Data Engineering (ICDE'2006).

[Raj01] K. Rajaraman, Ah-Hwee Tan: Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks. PAKDD 2001: 102-107

[ScW02] T. Scheffer and S. Wrobel. Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. Journal of Machine Learning Research, 3:833--862, 2002.

[SrS03] A. Srivastava and J. Stroeve, Onboard Detection of Snow, Ice, Clouds and Other Geophysical Processes Using Kernel Methods, Proceedings of the ICML’03 workshop on Machine Learning Technologies for Autonomous Space Applications

[SZZ01a] R. Sadri, C. Zaniolo, A. Zarkesh, J. Adibi Optimization of pattern matching Queries on Database Sequences, PODS'2001, Twentieth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Santa Barbara, May 21-24 2001

[SZZ01b] R. Sadri, C. Zaniolo, A. Zarkesh, J.Adibi A Sequential Pattern Query language for Supporting Instant Data Mining for e-Services, 27th International COnference on Very large Databasese (VLDB-2001), September, 11-14, 2001, Roma, Italy.

[TAC02] S. Tanner, M. Alshayeb, E. Criswell, M. Iyer, A. McDowell, M. McEniry, K. Regner, EVE: On-Board Process Planning and Execution, Earth Science Technology Conference, Pasadena, CA, Jun. 11 - 14, 2002

[TAH06] Dimitris K. Tasoulis, Niall M. Adams, David J. Hand, Unsupervised Clustering In Streaming Data. ICDM Workshops 2006: 638-642

[TCY03] W-G. Teng, M-S. Chen, and P.S. Yu, A Regression-based Temporal Pattern Mining Scheme for Data Streams , Proceedings of the International Conference on Very Large Data Bases, Berlin, Germany, Sept. 2003.

[TCY04] Wei-Guang Teng, Ming-Syan Chen, and Philip S. Yu, Resource-Aware Mining with Variable Granularities in Data Streams, Proceedings of SIAM International Conference on Data Mining 2004.

[TCZ03a] Nesime Tatbul, Ugur Cetintemel, Stan Zdonik, Mitch Cherniack and Michael Stonebraker, Load Shedding in a Data Stream Manager Proceedings of the 29th International Conference on Very Large Data Bases (VLDB), September, 2003.

[TCZ03b] N. Tatbul, U. Cetintemel, S. Zdonik, M. Cherniack, M. Stonebraker. Load Shedding on Data Streams, In Proceedings of the Workshop on Management and Processing of Data Streams (MPDS 03), San Diego, CA, USA, June 8, 2003.

[WFU03] H. Wang, W. Fan, P. Yu and J. Han, Mining Concept-Drifting Data Streams using Ensemble Classifiers, in the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Aug. 2003, Washington DC, USA.

[WZF03] K. Wang, S. Zhou, A. Fu, J. Yu. Mining changes of classification by correspondence tracing. SIAM International Conference on Data Mining 2003, May 2003, San Francisco.

[ViN02] S. D. Viglas and Jeffrey F. Naughton Rate based query optimization for streaming information sources. In Proc. of SIGMOD, 2002

[ZCW03] A. Zhou, Z. Cai, L. Wei, and W. Qian. M-Kernel Merging: Towards Density Estimation over Data Streams. In Proceedings of the 8th International Conference on Database Systems for Advanced Applications (DASFAA’2003), 2003.

[ZGT02a] D. Zhang, D. Gunopulos, V. J. Tsotras and B. Seeger, Temporal and Spatio-Temporal Aggregations over Data Streams using Multiple Time Granularities, Journal of Information Systems, vol. 27, no. 8, 2002.

[ZGT02b] D. Zhang, D. Gunopulos, V. J. Tsotras and B. Seeger, Temporal Aggregation over Data Streams using Multiple Granlarities, Proc. of 8th International Conference on Extending Database Technology (EDBT), Prague, Czech Republic, 2002.

[ZhS02] Y. Zhu and D. Shasha. StatStream: Statistical monitoring of thousands of data streams in real time. In VLDB 2002, pages 358--369.

[ZhS03] Y. Zhu and D. Shasha Efficient Elastic Burst Detection in Data Streams The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD-2003 24 August 2003 - 27 August 2003.

Dr. Eamonn Keogh is maintaining the largest Time Series Datasets

Last updated: March 26, 2008.





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