多类关联规则生成算法
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摘要
针对传统关联规则算法产生的规则关联性弱、种类少的缺点,结合Spearman秩相关系数,提出了一种多类关联算法。该算法在传统算法产生的强规则基础上,利用Spearman秩相关方法计算出规则中产品间的同步异步等相关性。将其作为兴趣度阈值,算法可同时产生同步正规则、异步正规则、同步负规则和异步负规则四类关联规则,且规则间联系紧密。实验结果表明了算法的有效性和优越性。
The association rules generated by traditional algorithms have the shortcomings of few classes and low correlation.Based on the analysis of these shortcomings,and combined with Spearman rank correlation coefficient,a new multi-class association rule algorithm was proposed.Based on the strong association rules generated by traditional algorithms,the new algorithm used Spearman rank correlation to calculate the synchronous and asynchronous correlation coefficient.Setting the correlation coefficient as the interest threshold,the new algorithm can generate synchronous positive rules,contrary positive rules,synchronous negative rules and contrary negative rules.Experiment has been carried out to illustrate the effectiveness and superiority of the algorithm.
The association rules generated by traditional algorithms have the shortcomings of few classes and low correlation.Based on the analysis of these shortcomings,and combined with Spearman rank correlation coefficient,a new multi-class association rule algorithm was proposed.Based on the strong association rules generated by traditional algorithms,the new algorithm used Spearman rank correlation to calculate the synchronous and asynchronous correlation coefficient.Setting the correlation coefficient as the interest threshold,the new algorithm can generate synchronous positive rules,contrary positive rules,synchronous negative rules and contrary negative rules.Experiment has been carried out to illustrate the effectiveness and superiority of the algorithm.
引文
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[2]SRIKANT R,AGRAWAL R.Mining generalized association rules[C]//VLDB'95:Proceedings of the 21st International Conferenceon Very Large Data Bases.San Francisco:Morgan Kaufmann,1995:407-419.
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[4]JALALVAND A,MINAEI B,ATABAKI G,et al.A new interest-ingness measure for associative rules based on the geometric context[C]//ICCIT'08:Proceedings of the 2008 Third International Con-ference on Convergence and Hybrid Information Technology.Wash-ington,DC:IEEE Computer Society,2008:199-203.
[5]曾安平,黄永平,李广军,等.基于协方差的FP_Growth算法在ERP中的研究与应用[J].数学的实践与认识,2008,38(12):11-18.
[6]周欣,沙朝锋,朱扬勇,等.兴趣度关联规则的又一阈值[J].计算研究与发展,2000,37(5):627-633.
[7]马建庆,钟亦,张世永.基于兴趣度的关联规则挖掘算法[J].计算机工程,2006,32(17):121-122.
[8]梅志芳,王建.关联规则兴趣度问题研究[J].计算机工程,2010,36(1):38-42.
[9]丁一,付弦.基于兴趣度的关联规则挖掘研究[J].情报科学,2011,29(6):939-942.
[10]陈晶,李正媛,王丽娜,等.一种地震目录中基于兴趣度的关联规则分析方法[J].计算机应用研究,2011,28(6):2078-2081.
[11]胡春玲,吴信东.基于贝叶斯网络的频繁模式兴趣度计算及剪枝[J].软件学报,2011,22(12):2934-2950.
[12]李裕奇,赵联文,王沁,等.非参数统计方法[M].成都:西南交通大学出版社,2010:116-119.
[13]邵峰晶,于忠清,王金龙,等.数据挖掘原理与算法[M],北京:科学出版社,2009:92-99.
[14]MICHAEL J A,GORDON S L.数据挖掘技术:市场营销销售与客户关系管理领域应用[M].2版.别荣芳,尹静,邓六爱,译.北京:机械工业出版社,2006.
[15]HAN J W,KAMBER M.数据挖掘概念与技术[M].范明,孟小峰,译.北京:机械工业出版社,2007:321-325.
[16]梁开健,梁泉,杨炳儒.关联规则挖掘中阈值协调器的设计与实现[J].系统工程与电子技术,2005,27(10):1800-1802.
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