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基于分布式概念格的分类规则挖掘 被引量：2

Mining classification rules based on the distributed concept lattice
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摘要 以概念格为分类模型,引入知识合并思想,并针对大规模数据的分类求解以及过拟合问题引入剪枝策略,从而得到分类剪枝概念格模型,在此基础上提出了基于分布式概念格模型的强分类规则提取算法;通过理论证明了算法的正确性,并通过实验证明了算法的可行性。 The algorithm of mining strong classification rules based on the distributed extended concept lattice（ECL） is presented, in which the concept lattice is used as the classification model and the method of knowledge combination is adopted. In order to mine strong classification rules in large scale databases, the mechanism of pruning is imported to handle the problem of overfitting. The correctness of the algorithm is proved by theory and its feasibility is shown by experiment.
作者 胡学钢 陈慧 张玉红 马冯
机构地区 合肥工业大学计算机与信息学院
出处 《合肥工业大学学报（自然科学版）》 CAS CSCD 北大核心 2007年第2期132-136,共5页 Journal of Hefei University of Technology：Natural Science
基金 安徽省自然科学基金资助项目(050420207) 合肥工业大学科研发展基金资助项目(050504F)
关键词 分类规则 分布式数据挖掘 概念格 过拟合 剪枝 classification rule distributed data mining concept lattice overfitting pruning
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15

  • 1Bueti G,Congiusta A,Talia D.Developing distributed data mining applications in the knowledge grid framework[A].High Performance Computing for Computational Science-VECPAR 2004-6th International Conference,Valencia,Spain,June 28-30,2004[C].2004:156-169.
  • 2Cannataro M,Congiusta A.Distributed data mining on grids:services,tools and applications[J].IEEE Transactions on Systems,Man and Cybernetics,Part B,2004,34(6):2451-2465.
  • 3Cannataro M,Talia D,Trunfio P.Distributed data mining on the grid[J].Future Generation Computer Systems,North-Holland,2002,18(8):1101-1112.
  • 4Cheung D W,Han Jiawei.Fast distributed algorithm for mining association rules[A].Parallel and Distributed Information Systems-Proceedings of the International Conference,1996[C].1996:31-42.
  • 5Merugu S,Ghosh J.A privacy-sensitive approach to distributed clustering[J].Pattern Recognition Letters,2005,26(4):399-410.
  • 6Garg M,Shyamasundar R K.A distributed clustering framework in mobile ad hoc networks[A].Proceedings of the International Conference on Wireless networks,ICWN'04,2004[C].2004:32-38.
  • 7Kargupta H.Distributed clustering using collective principal component analysis[J].Knowledge and Information Systems,2001,3(4):422-448.
  • 8Shafer J,Agrawal R,Mehta M.Sprint:a scalable parallel classifier for data mining[A].Int'l Conf on Very Large Databases,March 1996[C].1996:544-555.
  • 9Joshi M,Karypis G,Kumar M.ScalParC:a new scalable and efficient parallel classification algorithm for mining large datasets[A].Int'l Parallel Processing Symposium,Orlando,Florida,USA,April,1998[C].1998:573-579.
  • 10宾宁,李宏,陈松乔.基于SPRINT分类算法的异构分布式数据挖掘研究[J].计算机测量与控制,2005,13(1):76-78. 被引量：6

二级参考文献18

  • 1M.James. Classification Algorithms [M]. Wiley,1985.
  • 2Jiawei Han,Micheline Kamber.Data mining.concepts andtechniques[M]. Simo Fraser University,2000:187-198.
  • 3John Shafer, Rakesh AgrawaL SPRINT: A Scalable Parallel Classifier for Data Mining [J]. Proceedings of the 22nd VLDB Conference Mumbai (Bombay), India, 1996.
  • 4Bowyer K. A parallel decision tree builder for mining very large visualization datasets[J]. Proceedings of the IEEE International Conferenceon Systems, Man and Cybernetics,2000,3:1888-1893.
  • 5Agrawal R, Imielinski T, Swmi A. Database mining: a performance perspective [J]. IEEE Trans. On Knowledge and Data Engineering,1993, 5(6) :914-925.
  • 6Rakesh Agrawal, Sakti Ghosh, Tomasz Imielinski, Arun Swami. An interval classifier for database mining application [J]. In Proc. of the VLDB Conference, Vancouver, British Columbia, Canada, 1992:560-573.
  • 7Ganter B,Wille R.Formal Concept Analysis:Mathematical Foundations[M].Berlin:Springer-Verlag,1999.
  • 8Baltasar Fernandez-Manjon,Alfredo Fernandez-Valmayor.Building educational tools based on formal concept analysis[J].Education and Information Technologies,1998,3(3-4):187-201.
  • 9U Krohn,N J Davies,R Weeks.Concept lattices for knowledge management[J].BT Technol J,1999,17(4):108-113.
  • 10S O Kuznetsov.Machine learning on the basis of formal concept analysis[J].Automation and Remote Control,2001,62(10):1543-1564.

共引文献53

同被引文献19

  • 1曲立平,刘大昕,杨静,张万松.基于属性的概念格快速渐进式构造算法[J].计算机研究与发展,2007,44(z3):251-256. 被引量：9
  • 2王浩,胡学钢,赵文兵.基于量化相对约简格的分类规则发现[J].复旦学报（自然科学版）,2004,43(5):761-764. 被引量：2
  • 3张继福,张素兰,胡立华.约束概念格及其构造方法[J].智能系统学报,2006,1(2):31-38. 被引量：14
  • 4Troy A D,Zhang Guo-Qiang,Tian Ye.Faster concopt analysis[C[//LNM 4604:Conceptual Structures:Knowledge Architectures for Smart Applications.Berlin Heidelberg:Springer-Verlag,2007:206-219.
  • 5Wille R.Reconstructing lattice theory:An approach based on hierarchies of concepts[M]//Rival I.Odered Sets.Reidel,Dordrecht-Boston:[s.n.],1982:445-470.
  • 6Quinlan J R.Induction of decision trees[J].Machine Learning,1986,1(1):81-106.
  • 7Quinlan J R.C4.5:Programs for machine learning[M].San Francisco,CA:Morgan Kaufmann Publishers Inc,1993.
  • 8Rumelhart D E,Hinton G E,Williams R J.Lesrning internal representations by error propagation[C]//Parallel Distributed Processing:Explorations in the Microstructure of Cognition.Cambridge,MA:MIT Press,1986(1):318-362.
  • 9Sahami M.Learning classification rules using lattices[C]//Proceedings of the Eighth European Conference on Machine Learning,ECML-95,1995,1 (1):343-346.
  • 10Njiwoua P,Nguifo.E M.Forwarding the choice of bias LEGAL-F:Using feature selection to reduce the complexity of LEGAL[C]//Proceedings BENELEARN-97,ILK and INFOLAB.Netherlands:Tilburg University,1997:89-98.

引证文献2

二级引证文献3

合肥工业大学学报（自然科学版）
2007年 第2期

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