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基于图的多关系数据挖掘理论研究与方法

Graph-based Multi-relations Data Mining Fundamental Research and Application
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摘要 在过去的几年.结构化数据挖掘的需求日渐兴起,图是计算机学科和离散数学中最好的结构数据研究之一,基于图的数据挖掘已越来越广泛。本文介绍了基于图的数据挖掘的理论基础及其研究方法。 The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years. This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-hased data mining and brief descriptions of some representative approaches.
出处 《计算机科学》 CSCD 北大核心 2008年第5期152-153,157,共3页 Computer Science
基金 国家自然科学基金(60675030)资助 教育部科技重点项目(教技司[2000]175)资助
关键词 路径 结构化数据 数据挖掘 Graph, Tree, Path, Structured data, Data mining
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  • 1Mckay B. Nauty users guide (version 1.5). Technical Report, TR-CS-90-02. Department of Computer Science, Australian National University, 1990.
  • 2Inokuchi A, Washio T, Motoda H. Complete mining of frequent pattems from graphs: Mining graph data. Machine Leaming, 2003,50:321-354.
  • 3De Raedt L, Kramer S. The levelwise version space algorithm and its application to molecular fragment finding//IJCAI'01: Seventeenth International Joint Conference on Artificial Intelligence, vol2, 2001,853-859.
  • 4Agrawal R, Srikant R. Fast algorithms for mining association rules//VLDB'94: Twentyth Very Large Dada Base Conference. 1994.487-499.
  • 5Yoshida K, Motoda H, Indurkhya N. Graph-based induction as a unified learning framework J of Applied Intel, 1994,4:297-328.
  • 6Cook J, Holder L. Substructure discovery using minimum description length and background knowledge. J Artificial Intel Research, 1994,1 : 231-255.
  • 7Cook D J, Holder B. Graph-based Data Mining. IEEE,2000,15 (2) :32-41.
  • 8Cook J, Holder L B, Djoko S. Scalable discovery of informative structural concepts using domain know] edge. IEEE Expert, 1996,11(15).
  • 9Agrawal R, Srikant R. Fast algorithms for mining association rules//VLDB'94 : Twentyth Very Large Dada Base Conference. 1994:487-499.
  • 10De Raedt L, Kramer S. The levelwise version space algorithm and its application to molecular fragment finding//IJCAI'OI: Seventeenth International Joint Conference on Artificial Intelligence, vol2. 2001:853-859.

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