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基于空间分割的SWRL数据集关联规则挖掘

Search Space Partition-based Association Rules Mining from SWRL Data Set
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摘要 语义Web环境下的关联规则挖掘是数据挖掘领域新的研究热点。本文针对SWRL数据集的特征,建立新的数据挖掘形式背景,将FCA用于关系型关联规则的挖掘,提出了基于搜索空间分割的关联规则挖掘方法。采用FCA作为频繁模式的压缩表示方式,从生成的闭查询导出的关联规则,可有效控制冗余规则的产生。将搜索空间进行划分可减小问题的规模,充分利用已有的挖掘过程的中间结果所提供的信息,减少了计算量。由于采用了分而治之的策略,本文的方法易于扩展到对海量语义Web数据的并行处理。 Association rules mining in Semantic Web is a new challenge for data mining researchers. A search space partition based association rules mining method for SWRL data set is proposed. FCA is adopted as the condensed representation of frequent patterns with conjunctive query formation, association rules induced from closed query can avoid producing redundant rules in semantic. Search space partition can divide the large scale problem into small parts, computation results obtained during mining procedure are also be used fully for reducing unnecessary computation. The method proposed can easily be adapted to parallel algorithm for processing large mount of Semantic Web Data because of the strategy of divide and conquer.
作者 袁柳 李战怀 陈世亮
机构地区 西北工业大学计算机学院
出处 《计算机科学》 CSCD 北大核心 2008年第5期147-151,共5页 Computer Science
基金 国家自然科学基金项目资助(60573096)
关键词 SWRL FCA 数据集 关联规则 SWRL, FCA, Association rules
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16

  • 1http ://www. w3. org/Submission/SWRL/.
  • 2Zaki M. Efficiently Mining Frequent Trees in a Forest// Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada, 2002 . 68-75.
  • 3Kuramochi M,Karypis G. Frequent Subgraph Discovery//Proceedings of the 2001 IEEE International Conference on Data Mining. San Jose, California,.USA, 2001-3.13-320.
  • 4Pasquier N,Bastide Y,Taouil R,et al. Efficient Mining of Association Rules Using Closed Itemset Lattices. Journal of Information Systems, 1999, 24(1): 25-46.
  • 5Pasquier N, Bastide Y, Taouil R, et al. Discovering Frequent Closed Itemsets for Association Rules//Proceedings of the 7th International Conference on Database Theory. Jerusalem, Israel, 1999:398-416.
  • 6Bastide Y, Taouil R, Pasquier N, et al. Mining Frequent Patterns with Counting Inference. SIGKDD Explorations, Special Issue on Scalable Algorithms, 2000,2(2) :66-75.
  • 7Pei J, Han J, Mao R. CLOSET:An efficient algorithm for min- ing frequent closed itemsets//The Proceeding of ACMSIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. 2000 :21-30.
  • 8Stumme G, Taouil R,Bastide Y, et al. Computing Iceberg Concept Lattices with Titanic. Knowledge and Data Engineering (KDE), 2002,42(2) : 189-222.
  • 9Dehaspe L, Toivonen H. Discovery of Frequent Datalog Pat- terns. Data Mining and Knowledge Discovery, 1999,3(1):7-36.
  • 10Lisi F A, Malerba D. Inducing Multi-level Association Rules from Multiple Relation. Machine Learning Journal, 2004, 55: 175-210.

二级参考文献10

  • 1Ganter B, Wille R. Formal Concept Analysis: Mathematical Foundations. Berlin Heidelberg: Springer-Verlag, 1999.
  • 2Carpineto C, Romano G. A lattice conceptual clustering system and its application to browsing retrieval. Machine Learning, 1996,24:95-122.
  • 3Stumme G, Wille R, Wille U. Conceptual knowledge discovery in databases using formal concept analysis methods. In: Zytkow JM,Quafafou M, eds. Proc. of the 2nd European Symp. on Principles of Data Mining and Knowledge Discovery (PKDD'98). LNCS1510, Berlin: Springer-Verlag, 1998. 450-458.
  • 4Arevalo G, Mens T. Analysing object-oriented application frameworks using concept analysis. In: Bruel JM, Bellahsene Z, eds.Proc. of the Advances in Object-Oriented Information Systems (OOIS 2002) Workshops. LNCS 2426, Berlin: Springer-Verlag,2002. 53-63.
  • 5Baader F, Sertkaya B. Applying formal concept analysis to description logics. In: Eklund P, ed. Proc. of the 2nd Int'l Conf. on Formal Concept Analysis (ICFCA 2004). LNC S 2961, Berlin: Springer-Verlag, 2004.261-286.
  • 6Kuznetsov SO, Obiedkov SA. Comparing performance of algorithms for generating concept lattices. Journal of Experimental and Theoretical Artificial Intelligence, 2002,14(23 ): 189-216.
  • 7Ganter B. Formal concept analysis: Algorithmic aspects. 2002. http:www.math.tu-dresden.de/～ganter/cl02/
  • 8Godin R, Missaoui R, Alaoui H. Incremental concept formation algorithms based on Galois lattices. Computation Intelligence,1995,11(2):246-267.
  • 9Blake CL Merz CJ. UCI repository of machine learning databases. Department of Information and Computer Science, Irvine:University of California, 1998. http://www.ics.uci.edu/～mlearn/MLRepository.html
  • 10Fu H, Nguifo EM. A parallel algorithm to generate formal concepts for large data. In: Eklund P, ed. Proc. of the 2nd Int'l Conf. on Formal Concept Analysis (ICFCA 2004). LNCS 2961, Berlin: Springer-Merlag, 2004. 394-401.

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计算机科学
2008年 第5期

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