[1]马彦勤,武彤,邓烜堃.基于兴趣度度量的正负关联规则挖掘方法研究[J].计算机技术与发展,2018,28(05):38-41.[doi:10.3969/j.issn.1673-629X.2018.05.009]
　MA Yanqin,WU Tong,DENG Xuankun.Research on Mining Method of Positive and Negative Association Rules Based on Interestingness Measurement[J].,2018,28(05):38-41.[doi:10.3969/j.issn.1673-629X.2018.05.009]
点击复制

基于兴趣度度量的正负关联规则挖掘方法研究()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
38-41
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
Research on Mining Method of Positive and Negative Association Rules Based on Interestingness Measurement
文章编号:
1673－629X(2018)05－0038－04
作者:
马彦勤; 武彤; 邓烜堃
贵州大学 计算机科学与技术学院，贵州 贵阳 550025
Author(s):
MA Yan－qin; WU Tong; DENG Xuan－kun
School of Computer Science and Technology，Guizhou University，Guiyang 550025，China
关键词:
支持度; 置信度; 兴趣度度量; 正负关联规则; 数据挖掘
Keywords:
 ; support; confidence; interestingness measurement; positive and negative association rules; data mining
分类号:
TP301.6
DOI:
10.3969/j.issn.1673-629X.2018.05.009
文献标志码:
A
摘要:
传统的 Apriori 算法通过支持度阈值和置信度阈值的筛选来挖掘强关联规则，所挖掘出来的强关联规则并不都是有趣的，也忽略了负关联规则的挖掘，失去了负关联规则在决策分析中的重要作用。为了过滤强关联规则中的无趣规则，挖掘有趣的正负关联规则，引入了兴趣度的概念，对现有的几种兴趣度度量进行了研究，利用兴趣度度量的相关性质，提出了一种新的兴趣度度量。根据支持度、置信度、兴趣度的相关性质提出了相关定理并进行证明，用以挖掘有趣的正负关联规则。在新的兴趣度度量的基础上进行了算法设计，并采用真实数据集进行算法验证。结果显示，以提出的兴趣度度量为基础进行正负关联规则的挖掘是可行的，其结果比经典的 Apriori 算法挖掘方法更实用更有效。
Abstract:
The mining of traditional Apriori algorithm is for strong association rules through support and confidence threshold．The rules mined are not all interesting，and also ignore the mining of negative association rules and lose the important role of the negative association rules in decision analysis．In order to filter the uninteresting rules in strong association rules and excavate interesting positive and negative association rules，the concept of interestingness is introduced．We propose a new interestingness measurement based on research on several interestingness measurements with some related properties of them．The related theorems are proposed and proved according to the relative properties of support degree，confidence degree and interestingness degree to excavate the interesting positive and negative association rules．Based on the new interestingness measurement，the algorithm is designed and the experiment is carried out with real data．The results show that the mining of positive and negative association rules based on the proposed interestingness measure is more practical and effective than the classical Apriori algorithm．

相似文献/References:

[1]郑春香 韩承双 董甲东.关联规则技术在教学评价中的应用[J].计算机技术与发展,2009,(09):215.
　ZHENG Chun-xiang,HAN Cheng-shuang,DONG Jia-dong.Application of Association Rule Mining in Teaching Appraisal[J].,2009,(05):215.
[2]胡健 罗家国 杨炳儒.基于信息扩张机制的意外规则处理新方法研究[J].计算机技术与发展,2008,(12):74.
　HU Jian,LUO Jia-guo,YANG Bing-ru.Research on Exceptional Rules Selecting and Comprehensibility Based on Information Increasing Mechanism[J].,2008,(05):74.
[3]程玉胜 邓小光 江效尧.Apriori算法中频繁项集挖掘实现研究[J].计算机技术与发展,2006,(03):58.
　CHENG Yu-sheng,DENG Xiao-guang,JIANG Xiao-yao.Realization of Mining Frequent Itemsets Based on Apriori[J].,2006,(05):58.
[4]张璐璐 贾瑞玉 李杰.一种基于规则的离群挖掘算法[J].计算机技术与发展,2006,(12):73.
　ZHANG Lu-lu,JIA Rui-yu,LI Jie.An Algorithm for Outliers Mining Based on Rule.[J].,2006,(05):73.
[5]魏欣 蒋华伟.基于纹理和结构的图像修复算法研究[J].计算机技术与发展,2010,(09):90.
　WEI Xin,JIANG Hua-wei.Study on Inpainting Algorithm Based on Image Structure and Texture[J].,2010,(05):90.
[6]王代星 张小平 王翰虎.基于置信度与隐含度削减的Apriori算法改进[J].计算机技术与发展,2010,(11):105.
　WANG Dai-xing,ZHANG Xiao-ping,WANG Han-hu.Improved Apriori Algorithm Based on Prune Using Confidence and Implicit[J].,2010,(05):105.
[7]鄂旭,任骏原,毕嘉娜,等.基于粗糙变精度的食品安全决策树研究[J].计算机技术与发展,2014,24(01):242.
　E Xu[][],REN Jun-yuan[],BI Jia-na[],et al.Research on Decision Tree for Food Safety Based on Variable Precision Rough Sets[J].,2014,24(05):242.
[8]杨井荣,侯向宁.正负关联规则数据挖掘算法研究[J].计算机技术与发展,2020,30(11):64.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 012]
　YANG Jing-rong,HOU Xiang-ning.Research on Data Mining Algorithm Based on Positive and Negative Association Rules[J].,2020,30(05):64.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 012]
[9]刘昊东,王 诚.基于热门度修正因子和置信度的协同过滤算法[J].计算机技术与发展,2023,33(03):127.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 019]
　LIU Hao-dong,WANG Cheng.Collaborative Filtering Algorithm Based on Popularity Correction Factor and Confidence[J].,2023,33(05):127.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 019]
[10]黄名选 陈燕红 张师超[].基于关联规则挖掘的查询扩展检索性能研究[J].计算机技术与发展,2008,(10):103.
　HUANG Ming-xuan,CHEN Yan-hong,ZHANG Shi-chao.Studies on Retrieval Performance of Query Expansion Based on Association Rules Mining[J].,2008,(05):103.
[11]伊瑶瑶,茅苏. Hadoop下的关联规则分析研究[J].计算机技术与发展,2015,25(09):84.
　YI Yao-yao,MAO Su. Research on Association Rules Analysis under Hadoop Platform[J].,2015,25(05):84.

更新日期/Last Update: 2018-06-28