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Aggarwal CC. Data streams: models and algorithms. Kluwer Academic; 2006.
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Aggarwal CC, Han J, Wang J, Yu PS. On demand classification of data streams. In: Proceedings of the 10th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining; 2004. p. 503–8.
Babcock B, Babu S, Datar M, Motwani R, Widom J. Models and issues in data stream systems. In: Proceedings of the 21st ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2002. p. 1–16.
Cai YD, Clutter D, Pape G, Han J, Welge M, Auvil L. MAIDS: mining alarming incidents from data streams. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2004. p. 919–20.
Chen Y, Dong G, Han J, Wah BW, Wang J. Multi-dimensional regression analysis of time-series data streams. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 323–34.
Cormode G, Muthukrishnan S. What’s hot and what’s not: tracking most frequent items dynamically. In: Proceedings of the 22nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2003. p. 296–306.
Gao J, Fan W, Han J, Yu PS. A general framework for mining concept-drifting data streams with skewed distributions. In: Proceedings of the SIAM International Conference on Data Mining; 2007.
Guha S, Mishra N, Motwani R, O’Callaghan L. Clustering data streams. In: Proceedings of the 41st Annual Symposium on Foundations of Computer Science; 2000. p. 359–66.
Hulten G, Spencer L, Domingos P. Mining time-changing data streams. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001.
Kargupta H, Bhargava B, Liu K, Powers M, Blair P, Bushra S, Dull J, Sarkar K, Klein M, Vasa M, Handy D. VEDAS: a mobile and distributed data stream mining system for real-time vehicle monitoring. In: Proceedings of the SIAM International Conference on Data Mining; 2004.
Manku G, Motwani R. Approximate frequency counts over data streams. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 346–57.
Mendes L, Ding B, Han J. Stream sequential pattern mining with precise error bounds. In: Proceedings of the 2008 IEEE International Conference on Data Mining; 2008.
O’Callaghan L, Meyerson A, Motwani R, Mishra N, Guha S. Streaming-data algorithms for high-quality clustering. In: Proceedings of the 18th International Conference on Data Engineering; 2002. p. 685–96.
Shasha D, Zhu Y. High performance discovery in time series: techniques and case studies: Springer; 2004.
Wang H, Fan W, Yu PS, Han J. Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the 9th ACM SIGKDD International Conferenc on Knowledge Discovery and Data Mining; 2003. p. 226–35.
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Han, J., Ding, B. (2018). Stream Mining. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_369
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