| 基于相关性度量的关联规则挖掘 |
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| 引用本文： | 吕杰林,陈是维.基于相关性度量的关联规则挖掘[J].浙江大学学报(理学版),2012,39(3):284-288. |
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| 作者姓名： | 吕杰林 陈是维 |
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| 作者单位： | 1. 浙江商业职业技术学院,浙江杭州,310053
2. 浙江大学数学系,浙江杭州,310027 |
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| 摘 要： | 传统的关联规则挖掘方法容易生成一些无用规则,甚至生成误导规则,也不能区分正负关联规则.通过相关性度量,建立了基于相关性度量的兴趣度模型,并利用兴趣度模型改进了关联规则算法,最后,通过实例验证了此算法不仅能够避免生成无用规则和误导规则,还能生成一些感兴趣的负关联规则.
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| 关 键 词： | 数据挖掘 关联规则 相关性度量 兴趣度 |
Mining association rules based on correlation measure |
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| L Jie-lin
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CHEN Shi-wei.Mining association rules based on correlation measure[J].Journal of Zhejiang University(Sciences Edition),2012,39(3):284-288. |
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| Authors: | L Jie-lin CHEN Shi-wei |
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| Institution: | 1.Zhejiang Vocational College of Commerce,Hangzhou 310053,China; 2.Department of Mathematics,Zhejiang University,Hangzhou 310027,China) |
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| Abstract: | The traditional method of data mining based on association rules can easily bring about some useless and even misleading rules,and it also fails to differentiate between the positive association rules and the negative ones.This paper aims to solve these problems.First the interest model is built up on the basis of the correlation measure,then by utilizing this model the algorithm of association rules is greatly improved,and lastly experimental results have proved that this algorithm can avoid useless and misleading rules and generate some interesting negative association rules. |
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| Keywords: | data mining association rules correlation measure interest measure |
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