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数据流挖掘算法研究综述 被引量：21

Survey on data stream mining
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摘要 流数据挖掘是数据挖掘的一个新的研究方向,已逐渐成为许多领域的有用工具。在介绍数据流的基本特点以及数据流挖掘的意义的基础上,对现有数据流挖掘算法的主要思想方法进行了总结,并指出了这些方法的局限性。最后对数据流挖掘的发展方向进行了展望。 Data stream mining is a new research aspect of data mining. It has be come a useful tool for many fields. The essential characteristic of data stream and the significance of data stream mining are introduced. The main ideal of existing data stream mining algorithms is summarized, and the limitation of the algorithms is pointed out. Some research directions about data stream mining in future work are put forward.
作者 蒋盛益 李庆华 李新
机构地区 华中科技大学计算机学院
出处 《计算机工程与设计》 CSCD 北大核心 2005年第5期1130-1132,1169,共4页 Computer Engineering and Design
基金 国家自然科学基金项目(60273075)
关键词 数据流 挖掘算法 聚类 分类 频繁模式 data stream data stream mining clustering, classification frequent pattern
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献16

  • 1Babcock B, Babu S, Datar M, et al. Models and issues in data stream systems[C]. Madison, Wisconsin, USA:Proc of ACM SIGMOD/SIGACT Conf on Princ of Database Syst. 2002.1-16.
  • 2O'Callaghan L, Mishra N, Meyerson A, et al. Streaming-data algorithms for high-quality clustering[C]. Proc of IEEE International Conference on Data Engineering, 2002.
  • 3Guha S, Mishra N, Motwani R, et al. Clustering data streams[C].Proc of IEEE Symposium on Foundations of Computer Science (FOCS'00), 2000.71-80.
  • 4Guha S, Meyerson A, Mishra N, et al. Clustering data streams:Theory and practice[J]. Knowledge and Data Engineering, IEEE Transactions, 2003, 15(3):515-528.
  • 5Giannella C, HAN Jia-wei, JIAN Pei, et al. Mining frequent patterns in data streams at multiple time granularities[C]. Proc of the NSF Workshop on Next Generation Data Mining, 2002.
  • 6Aggarwal C, Han J, Wang J, et al. A framework for clustering evolving data streams[C]. Berlin, Germany: Proc of Int Conf on Very Large Data Bases (VLDB'03), 2003.
  • 7Dora Cai Y, Clutter D, Pape G, et al. MAIDS mining alarming incidents from data streams[C].Paris, France:Proc of the 23rd ACM SIGMOD, 2004.
  • 8Dong G, Han J, LVS Lakshmanan, et al. Online mining of changes from data streams: Research problems and preliminary result [C], Proc of ACM SIGMOD Workshop on Management and Processing of Data Streams, 2003,.
  • 9何增有,徐晓飞,邓胜春.Squeezer：An Efficient Algorithm for Clustering Categorical Data[J].Journal of Computer Science & Technology,2002,17(5):611-624. 被引量：32
  • 10Portnoy L, Eskin L, Stolfo S J. Intrusion detection with unlabeled data using clustering[C]. Proc of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001), Philadelphia,2001.

二级参考文献17

  • 1Sudipto Guha, Rajeev Rastogi, Kyuseok Shim. ROCK: A robust clustering algorithm for categorical attributes. In Proc. 1999 Int. Conf. Data Engineering, Sydney, Australia, Mar., 1999, pp.512-521.
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  • 5Sudipto Guha, Rajeev Rastogi, Kyuseok Shim. CURE: A clustering algorithm for large databases. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Seattle, Washington, USA, June, 1998, pp.73-84.
  • 6Karypis G, Han E-H, Kumar V. CHAMELEON: A hierarchical clustering algorithm using dynamic modeling. IEEE Computer, 1999, 32(8): 68-75.
  • 7Sheikholeslami G, chatterjee S, Zhang A. WaveCluster: A multi-resolution clustering approach for very large spatial databases. In Proc. 1998 Int. Conf. Very Large Databases, New York, August, 1998, pp.428-439.
  • 8Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. the 1998 ACM SIGMOD Int. Conf. Management of Data, Seattle, Washington,USA, June, 1998, pp.94-105.
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共引文献31

同被引文献150

引证文献21

二级引证文献58

计算机工程与设计
2005年 第5期

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