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基于lazy方法的数量型关联分类 被引量：1

Quantitative associative classification based on lazy method
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摘要 传统关联分类方法处理数量型数据时,"先离散,再学习"的步骤使新的测试样例可能无法找到合适的离散区间,形成离散盲目性问题。基于lazy的数量型关联分类作为一种新的关联分类法,它首先利用K-近邻分类思想为测试样例求得K-近邻作为新的训练数据集,然后对包含测试样例和K个近邻的数据集离散化,并在K-近邻组成的离散数据集上挖掘关联规则并构造分类器进行分类。最后,通过与传统CBA、CMAR、CPAR算法在7个常用UCI数量型数据集上进行的对比实验结果表明,基于lazy的数量型关联分类方法的平均分类准确率提高了0.66%～1.65%,证明了该方法的可行性。 In order to avoid the problem of blind discretization of traditional classification ＂discretize first learn second＂,a new method of associative classification based on lazy thought was proposed.It discretized the new training dataset gotten by determining the K-nearest neighbors of test instance firstly,and then mined associative rules form the discrete dataset and built a classifier for predicting the class label of test instance.At last,the results of contrastive experiments with CBA（Classification Based on Associations）,CMAR（Classification based on Multiple Class-Association Rules） and CPAR（Classification based on Predictive Association Rules） carried out on seven commonly used quantitative datasets of UCI show that the classification accuracy of the proposed method can be increased by 0.66% to 1.65%,and verify the feasibility of this method.
作者 李学明 李宾飞 杨涛 付萌
机构地区 重庆大学计算机学院 中国科学技术大学软件学院
出处 《计算机应用》 CSCD 北大核心 2013年第8期2184-2187,共4页 journal of Computer Applications
基金 国家自然科学基金资助项目(61103114) 重庆市高等教育教学改革研究重点资助项目(112023) 中央高校基本科研业务基金资助项目(CDJXS11181164) "211工程"三期建设资助项目(S10218)
关键词 数据挖掘 lazy方法 数量型关联分类 关联规则 K-近邻 data mining lazy method quantitative associative classification associative rule K-Nearest Neighbors（KNN）
分类号 TP311 [自动化与计算机技术—计算机软件与理论] TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献17

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二级参考文献22

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共引文献207

同被引文献11

  • 1Thabtah F. A review of associative classification mining [ J ]. The Knowledge Engineering Review, 2007,22( 1 ):37-65.
  • 2Veloso A, Meira W, Zaki M J. Lazy association classifica- tion[ C]/! Proc. of the 6th IEEE International Conference on Data Mining. 2006:645-654.
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  • 4Li Wenmin, Han Jiawei, Pei Jian. CMAR: Accurate and efficient classification based on multiple class-association rules [ C ]//Proc. of IEEE International Conference on Da- ta Mining. 2001:369-376.
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  • 9Wang Ke, Zhou Senqiang, He Yu. Growing decision trees on support-less association rules [ C ]// Proc. of the 6th ACM SIGKDD International Conference on Knowledge Dis- covery and Data Mining. 2000:265-269.
  • 10张春生,李艳,庄丽艳,图雅,张玉春.基于局部性原理的分布式关联规则挖掘算法[J].计算机工程与应用,2012,48(21):143-145. 被引量：2

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计算机应用
2013年 第8期

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