期刊文献+

一种基于数据库查询改进决策树算法

An Improved Decision Tree Algorithm Based on Database Query
下载PDF
导出
摘要 在继承原有算法思路的基础上,对ID3算法的核心部分进行改进,通过使用嵌入式SQL,直接对目标数据库进行查询操作并处理,最终得到分类决策表并保存于数据库。试验证明,改进的ID3算法结合SQL的高效性和C语言的灵活性,能高效无缝地实现大量数据的分类且大大提高算法的执行效率。摘要: Proposes an improvement to the core section of ID3 based on the inherent ID3 algorithm.Using the embedded SQL,it directly queries the database and then processes received data,then finally acquires a decision table of classification.It is proved that with the improved ID3 combining the high efficiency of SQL and the flexibility of C language,can achieve the highly efficient and seamless classification of large data,and also greatly improve the processing efficiency.
作者 张国良
机构地区 [
出处 《现代计算机(中旬刊)》 2012年第1期18-21,31,共5页 Modern Computer
关键词 数据挖掘 决策树 ID3 嵌入式SQL 分类 Data Mining Decision Tree ID3 Embedded SQL Classification
  • 相关文献

参考文献7

  • 1HAN Jia-wei,Micheline Kamber.Data Mining.Concepts andTechniques[M].Morgan Kaufmann Publishers,2000.
  • 2Quinlan J R.Induction of Decision Trees[J].MachineLearn-ing,1986(4):81-106.
  • 3刘红岩,陆宏钧,陈剑.利用数据库技术实现的可扩展的分类算法[J].软件学报,2002,13(6):1075-1081. 被引量:14
  • 4UCI Machine Learning.http://mlearn.ics.uci.edu/MLReposito-ry.html.
  • 5WU Sen,WU Ling-yu,LONG Yu,GAO Xue-dong.ImprovedClassification Algorithm by Minsup and Minconf Based onID3[C].Management Science and Engineering,2006.ICMSE'06.2006 International Conference on Oct.5 2006-Sept.72006:135-139.
  • 6Jearanaitanakij,K.Classifying Continuous Data Set by ID3Algorithm[C].Information,Communications and Signal Pro-cessing,2005 Fifth International Conference on 06-09 Dec.2005:1048-1051.
  • 7乔梅,韩文秀.基于Rough集和数据库技术的属性约简算法[J].计算机工程,2005,31(6):18-19. 被引量:9

二级参考文献13

  • 1Pawlak Z. Rough Sets. International Journal of Computer and Information Science, 1982, 11(5): 341-356.
  • 2Skowron A.Rauszer C.The Discerni-bility Matrics and Functions in Information System. Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory. Dordrecht:Kluwer Academic Publishers, 1992:331-362.
  • 3Hu X H, Cercone N. Learning in Relational Databases: A Rough Set Approach. International Journal of Computational Intelligence, 1995,11(2): 323-338.
  • 4Guan J W, Bell D A. Rough Computational Methods for Information System. Artificial Intelligence, 1998,105(1-2):77-103.
  • 5Quinlan, J.R. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
  • 6Mehta, M., Agrawal, R., Rissanen, J. SLIQ: a fast scalable classifier for data mining. In: Apers, P., Bouzeghoub, M., Gardarin, G., eds. Proceedings of the 5th International Conference on Extending Database Technology. Berlin: Springer-Verlag, 1996. 18~32.
  • 7Wang, M., Iyer, B., Vitter, J.S. Scalable mining for classification rules in relational databases. In: Eaglestone, B., Desai, B.C., Shao, Jian-hua, eds. Proceedings of the 1998 International Database Engineering and Applications Symposium. Wales: IEEE Computer Society, 1998. 58~67.
  • 8Liu, B., Hsu, W., Ma, Y. Integrating classification and association rule mining. In: Agrawal, R., ed. Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining. New York: AAAI Press, 1998. 80~86.
  • 9Agrawal, R., Shim, K. Developing tightly-coupled data mining applications on a relational database system. In: Simoudis, E., ed. Proceedings of the 2nd International Conference on Knowledge Discovery in Databases and Data Mining. Cambridge, MA: AAAI Press, 1996. 112~118.
  • 10Meretakis, D., Wüthrich, B. Extending Naive Bayes classifiers using long itemsets. In: Chaudhuri, S., ed. Proceedings of the 5th International Conferenceon Knowledge Discovery and Data Mining. San Diego, CA: AAAI Press, 1999. 295~301.

共引文献19

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