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多步原子规则的大规模关联分类 被引量：1

Multistep classification based on atomic and associative rules in the large-scale datasets
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摘要 关联分类已成为数据挖掘研究的热点问题之一.为解决大规则关联分类问题,本文提出了基于原子规则的多步分类方案,并对作者提出的多步原子关联规则分类新技术进行了深入的理论研究.与同类关联分类方法(如CBA)比较,本文提出的方法具有学习速度快、分类准确度高的优点. The principles of multi-step classification based on atomic rules are described in this paper. A related scheme based on atomic rules is proposed to tackle the problem of associative classification in the context of large-scale datasets. Compared to the well-known associative classification algorithm of CBA, the proposed approach has the advantage of fast speed and high accuracy.
作者 许孝元 韩国强 闵华清
机构地区 华南理工大学计算机科学与工程学院
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2007年第3期471-474,共4页 Control Theory & Applications
基金 广东省科技攻关资助项目(2003C101007) 广州市科技计划基金资助项目(2004Z3-E0091).
关键词 数据挖掘 机器学习 关联规则 分类 data mining machine learning association rules classification
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献8

  • 1LIU B,HSU W,MA Y M.Integrating classification and association rule mining[C]∥Proc of the 4th Int Conf on Knowledge Discovery and Data Mining.New York:AAAI Press,1998,8:80-86.
  • 2LI W M,HAN J W,PEI J.CMAR:Accurate and efficient classification based on multiple class-association rules[C]∥Proc of the 1st IEEE Int Conf on Data Mining.New York:IEEE CS Press,2001,11:369-376.
  • 3YIN X X,HAN J W,CPAR:Classification based on predictive association rules[C]//Proc of the Third SIAM Int Conf on Data Mining.New York:IEEE CS Press.2003,5:331-335.
  • 4MARlA-LUIZA A,OSMAR R Z.An associative classifier based on positive and negative rules[C]//Proc of the 9th ACM SIGMoD Workshop on Research Issues in Data Mining and Knowledge Discovery.New York:IEEE CS Press.2004.6:64-69.
  • 5XIAOYUAN XU,GUOQIANG HAN,HUAQING MIN.Construct concise and accurate classifier by atomic association rules[C]//Proc of the Third Int Conf on Machine Learning and Cybernetics.New York:IEEE Press,2004,8:1604-1609.
  • 6CITATION R.HOLTE.Very simple classification rules perform well on most commonly used datasets[J].Machine Learning,1993,11(1):63-91.
  • 7STEPHEN D B,DENNI S F K,MICHAEL J P,et al.The UCI KDD archive of large data sets for data mining research and experimentation[J].SIGKDD Explorations,2000,2(2):81-85.
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同被引文献7

  • 1Holte CR. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 1993,(11):63 - 90.
  • 2Buddhinata G. Derry D. A Simple Enhancement to One Rule Classification. http://www.buddhinath.net/Other-Links/Docurnents/Improved%200neR%20Algorithm. pdf.
  • 3Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. Proceedings of 4th ACM Int. Conf. on KDD, New York, 1998:80 - 86.
  • 4Li WM, Han JW, Pei J. Cmar:Accurate and efficient classification based on multiple class-association rules. Proceedings of the IEEE Int. Conf. on Data Mining, California, 2001:369 - 376.
  • 5Yin XX, Han JW. Cpar:classification based on predicttive association rules. Proceedings of the 2003 SIAM International Conference on Data Mining (SDM'03),San Francisco, 2003:34 - 42.
  • 6Xu XY, Hart GQ, Min HQ. Construct concise and accurate classifier by atomic association rules.Proc of the Third Into Conf on Machine Learning and Cybernetics, New York: IEEE Press, 2004,8:1604- 1609.
  • 7胡可云,陆玉昌,石纯一.基于概念格的分类和关联规则的集成挖掘方法[J].软件学报,2000,11(11):1478-1484. 被引量：64

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控制理论与应用
2007年 第3期

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