期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

移动式数据挖掘平台模型 被引量：2

Mobile Data Mining Platform Model
下载PDF
导出
摘要 本文提出了一种移动式网络海量数据挖掘平台,给出了数据挖掘任务的形式化描述,详细讨论了平台的体系结构、工作原理与流程。该平台克服了传统网络计算模式的缺陷,支持对异质、分散数据源和知识源的挖掘和检索,具有开放性、可伸缩性、灵活性、可扩展性、健壮性。 A mobile data mining software platform for knowledge discovery on huge network databases is presented in this paper. The formal description of data mining task is defined and the ar-chitecture and workflow of the platform is detailed. This platform outperforms the traditional network computation platforms in that it supports knowledge discovery and data mining from heteroge-neous and distributed sources and it is more open, scalable, flexi-ble, extensible, and robust.
作者 刘君强 孙晓莹 杨传明 周志刚
机构地区 杭州商学院计算机系
出处 《微电子学与计算机》 CSCD 北大核心 2003年第8期82-84,103,共4页 Microelectronics & Computer
基金 浙江省自然科学基金(602140) 浙江省教育厅科技计划(20020635).
关键词 移动式数据挖掘平台模型 知识发现 移动型智能体 智能体 knowledge discovery data mining software model mobile agent
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献6

  • 1刘君强,孙晓莹,叶国荣.直接挖掘跨层关联规则的新方法[J].计算机工程与应用,2002,38(20):50-51. 被引量：8
  • 2刘君强.[D].浙江大学计算机科学与技术学院,2003.
  • 3刘君强.[D].浙江大学计算机科学与技术学院,2003.
  • 4Junqiang Liu, Yunhe Pan, Ke Wang, and Jiawei Han. Mining frequent item sets by opportunistic projection[A]. Proceedings of the 8th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining[C]. USA: ACM Press, 2002: 229-238.
  • 5Jiawei Hart, Yongjian Fu, et al. DBMiner: A System for Mining Knowledge in Large Relational Databases[A]. Proceedings of the 2nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C]. USA: ACM Press, 1996: 250-255.
  • 6Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques[M]. USA: Morgan Kanfinann Publishers,2000.

二级参考文献9

  • 1[5]J Han,J Pei,Y Yin. Mining frequent patterns without candidate gen eration[C].In: SIGMOD'2000, Dallas, TX, 2000
  • 2[6]R Agrawal,C Aggarwal,V V V Prasad. A tree projection algorithm for generation of frequent itemsets[J].In:Journal of Parallel and Distributed Compttting(Special Issue on High Performance Data Mining) ,2000
  • 3[7]R Srikant,R Agrawal. Mining quantitative association rules in largerelational tables[C].In:SIGMOD'96,Montreal ,Canada, 1996:1～12
  • 4[8]R J Bayardo. Efficiently mining long patterns from databases[C].In: SIGMOD'98, Seattle, Washington, 1998: 85～93
  • 5[9]N Pasquier,Y Bastide,R Taouil et al.Discovering frequent closed itemsets for association rules[C].In:ICDT'99,Jerusalem,Israel,1999: 398～416
  • 6[1]R Agrawal,R Srikant. Fast algorithms for mining association rules[C]. In: VLDB'94, Santiago, Chile, 1994: 487～499
  • 7[2]R Agrawal,T Imielinski,A Swami. Mining association rules between sets of items in large databases[C].In:SIGMOD'93,Washington,D C, 1993
  • 8[3]J Han,Y Fu. Discovery of multiple-level association rules from large databases[C].In: VLDB95, Zuich, Switzerland, 1995: 420～431
  • 9[4]R Srikant,R Agrawal. Mining generalized association rules[C].In :VL-DB'95, Zurich, Switzerland, 1995: 407～419

共引文献7

同被引文献14

  • 1lan Foster,Carl Kesselman.The Grid: Blueprint for a New Computing Infrastructure,Second Edition.USA: Morgan Kaufmann Publishers,2003.
  • 2R.MOORE.Knowledge-Based Grids.Technical Report TR-2001-02, San Diego Supercomputer Center.2001.
  • 3V. Ganti, et al. Mining Data Streams under Block Evolution.IEEE Computer, 1999.32(9):38-45
  • 4A. Arasu, et al. STREAM: The Stanford Stream Data Manager. In Proc. of the SIGMOD,2003.
  • 5S. Chandrasekaran and M. Franklin. Streaming Queries over Streaming Data. In Proc. of the VLDB,2002.
  • 6Y. Chen,et al.Multi-Dimensional Regression Analysis of Time-Series Data Streams.ln Proc. of VLDB,2002.
  • 7Junqiang Liu, Yunhe Pan, Ke Wang, Jiawei Han. Mining Frequent Item Sets by Opportunistic Projection. In Proc. Of the 8th ACM SIGKDD Intemalional Conference on Knowledge Discovery and Data Mining,2002.
  • 8G. Hulten, et al. Mining Time-Changing Data Streams. In Proc. 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,San Francisco,CA,USA,2001:97-106
  • 9G. S. Manku and R. Motwani. Approximate Frequency Counts over Data Streams. In Proc. of the 2002 ACM Symp. on Principles of Database Systems,2002.
  • 10A. Veloso, et al. Mining Frequent Itemsets in Evolving Databases. In Proc. of the SIAM Intemational Conference on Data Mining,2002.

引证文献2

二级引证文献3

微电子学与计算机
2003年 第8期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部