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基于时空划分的数据流挖掘 被引量：4

Data Stream Mining Based on Time and Space Partitioning
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摘要 基于时空划分的思想,设计概要数据结构的在线生成算法。概要数据结构保存流数据不同时刻的分布状态,以支持离线阶段的分类、聚类和关联规则发现等数据挖掘操作。研究时间粒度、量化向量调整和子区域索引等3项内存需求控制策略,以平衡概要数据结构的内存需求和内外存之间的I/O次数。 Based on the idea of time and space partitioning, this paper designs synopsis data structures which contains the distributed status of data stream to support different data mining tasks such as classifying, clustering and association rules discovery. Three kinds of measures are researched to control the potential huge requirement of memory caused by space partitioning, so that the synopsis＇ memory requirement and the number of I/O are balanced.
作者 袁正午 袁松彪
机构地区 重庆邮电大学中韩合作GIS研究所 重庆大学土木工程学博士后流动站
出处 《计算机工程》 CAS CSCD 北大核心 2010年第7期61-62,65,共3页 Computer Engineering
基金 国家"863"计划基金资助项目(2007AA12Z226) 重庆自然科学基金资助项目(CSTC2007BB2446)
关键词 数据流 时空划分 概要数据结构 聚类 data stream time and space partitioning synopsis data structure clustering
分类号 TP311.13 [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5

  • 1Han Jiawci,Kamber M.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2007.
  • 2Aggarwal C, Han Jiawei, Wang Jianyong. A Framework for Clustering Evolving Data Streams[C]//Proc. of the 29th International Conference on Very Large Data Bases. Berlin, Germany: [s. n.], 2003.
  • 3Yang Qiang, Wu Xindong. 10 Challenging Problems in Data Mining Research[J]. International Journal of Information Technology & Decision Making, 2006. 5(4): 597-604.
  • 4Sun Huanliang, Yu Ge, Bao Yubin, et al. CDS-Tree: An Effective Index tbr Clustering Arbitrary Shapes in Data Stream[C]//Proc. of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications. Washington, USA: IEEE Computer Society, 2005:81-88.
  • 5Parsons L, Haquc E, Liu Huan. Subspace Clustering for High Dimensional Data: A Review[J]. ACM SIGKDD Explorations Newsletter Archive, 2004, 6( 1 ): 90-105.

同被引文献26

  • 1林国平,陈磊松.一种网格和分形维数的数据流聚类算法[J].郑州大学学报（理学版）,2009,41(2):24-28. 被引量：2
  • 2周艳,朱庆,张叶廷.基于Hilbert曲线层次分解的空间数据划分方法[J].地理与地理信息科学,2007,23(4):13-17. 被引量：18
  • 3Aggarwal C C. Data Streams: Models and Algorithms[M]. Berlin, Germany: Springer, 2007.
  • 4Fan Wei, Huang Yian, Haixun Wang, et al. Active Mining of Data Streams[C]//Proc. of the 4th SIAM International Conference on Data Mining. Lake Buena Vista, Florida, USA: [s. n.], 2004:457-461.
  • 5Zhu Xingquan, Zhang Peng, Lin Xiaodong, et al. Active Learning from Data Streams[D]. Boca Raton, USA: Department of Computer Science and Engineering, Florida Atlantic University, 2007.
  • 6Hulten G, Spencer L, Domingos P. Mining Time-changing Data Streams[C]//Proc. of Int'l Conf. on Knowledge Discovery and Data Mining. San Francisco, USA: ACM Press, 2001.
  • 7Bifet A, Holmes G, Pfahringer B, et al. New Ensemble Methods for Evolving Data Streams[C]//Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [S. l.]: ACM Press, 2009: 139-148.
  • 8Fan Wen. Systematic Data Selection to Mine Concept-drift Data Streams[C]//Proc. of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Toronto, Canada: [s. n.], 2004: 128-137.
  • 9Li Huafu,Ho Chin-Chuan,Lee Suh-Yin,Incremental Updates of Closed Frequent Itemsets over Continuous Data Streams[J].Expert Systems with Applications,2009,36(2):2451-2458.
  • 10Teng Weiguang,Chen Ming-Syan,Yu Philip.A Regression-based Temporal Pattern Mining Scheme for Datastreams[C]//Proc.of the 29th International Conference on Very Large Databases.Berlin,Germany:Morgan Kaufmann,2003.

引证文献4

二级引证文献9

计算机工程
2010年 第7期

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