All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight
ZHOU Xiu-mei1, HUANG Ming-xuan2
1. Department of Mathematics and Computer Science, Nanning Prefecture Education College, Chongzuo, Guangxi 532200, China;
2. School of Information and Statistics, Guangxi University of Finance and Economics, Nanning, Guangxi 530003, China
This paper proposes a mining algorithm of all-weighted positive and negative association rules based on dynamic item weight, which can solve the problems of negative patterns mining based on dynamic item weight.This algorithm took the dynamic item weight dependent on transaction records into consideration, and adopted the itemset pruning method and pattern evaluation framework so as to discover effective all-weighted positive & negative association rules via simple calculation and comparison of weight ratio and dimension ratio from the itemset.The experimental results show that this algorithm can prevent ineffective patterns, which makes the maximal declines of the mining time and number of the candidate itemsets by up to 94.09% and 88.16% respectively compared with the existing unweighted positive and negative association rule mining algorithms.
周秀梅, 黄名选. 基于项权值变化的完全加权正负关联规则挖掘[J]. 电子学报, 2015, 43(8): 1545-1554.
ZHOU Xiu-mei, HUANG Ming-xuan. All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight. Chinese Journal of Electronics, 2015, 43(8): 1545-1554.
[1] Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large databases[A].Proceedings of 1993 ACM-SIGMOD International Conference on Management of Data( SIGMOD.93)[C].New York,USA:ACM Press,1993.207-216.
[2] 宋威,李晋宏,徐章艳,等.一种新的频繁项集精简表示方法及其挖掘算法的研究[J].计算机研究与发展,2010,47(2):277-285. Song Wei,Li Jin-hong,Xu Zhang-yan,et al.Research on a new concise representation of frequent itemset and its mining algorithm[J].Journal of Computer Research and Development,2010,47(2):277-285.(in Chinese)
[3] Nnrmadha D,Naveensundar G,Geetha S.Anovel approach to prune mined association rules in large databases[A].Proceeding of 2011 3rd International Conference on Electronics Computer Technology(ICECT)[C].Washington,USA:IEEE Computer Society Press,2011.409-413.
[4] Glass D H.Confirmationmeasures of association rule interestingness[J].Knowledge-Based Systems,2013(44):65-77.
[5] 耿生玲,李永明,刘震.关联规则挖掘的软集包含度方法[J].电子学报,2013,41(4):804-809. Geng Sheng-ling,Li Yong-ming,Liu Zhen.A approach to association rules mining using inclusion degree of soft sets[J].Acta Electronic Sinica,2013,41(4):804-809.(in Chinese)
[6] Shaheen M,Shahbaz M,Guergachi A.Context based positive and negative spatio-temporal association rule mining[J].Knowledge-Based Systems,2013,37(1):261-273.
[7] Wu X D,Zhang C Q,Zhang S C.Efficient mining of both positive and negative association rules[J].ACM Transactions on Information Systems,2004,22(3):381-405.
[8] Swesi I M A O,Bakar A A,Kadir A S A.Mining positive and negative association rules from interesting frequent and infrequent itemsets[A].Proceedings of 9th International Conference on Fuzzy Systems and Knowledge Discovery(FSKD 2012)[C].Washington,USA:IEEE Computer Society Press,2012.650-655.
[9] Bhargava R,Lade S.Effectivepositive negative association rule mining using improved frequent pattern tree[J].International Journal of Advanced Research in Computer Science and Software Engineering,2013,3(4):193-199.
[10] Cai C H,Da A,Fu W C,et al.Mining association rules with weighted items[A].Proceedings of IEEE International Database Engineering and Application Symposiums[C].Washington,USA:IEEE Computer Society Press,1998.68-77.
[11] Yun U,Ryu K H.Approximate weightedfrequent pattern mining with/without noisy environments[J].Knowledge-Based Systems,2011(24):73-82.
[12] Pears R,Koh Y S.Weighted association rule mining using particle swarm optimization[A].Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD) Workshops[C].Berlin,Germany:Pringer-Verlag,2012.327-338.
[13] Tan J.Weighted association rules mining algorithm research[J].Applied Mechanics and Materials,2013,241-244:1598-1601.
[14] Zhao Y Y,Jiang H,Geng R,et al.Mining weighted negative association rules based on correlation from infrequent items[A].Proceedings of the 2009 International Conference on Advanced Computer Control[C].Los Alamitos,California,USA:IEEE Computer Society Press,2009.270-273.
[15] 谭义红,林亚平.向量空间模型中完全加权关联规则的挖掘[J].计算机工程与应用,2003(13):208-211. Tan Yi-hong,Lin Ya-ping.Mining all-weighted association rules from vector space model[J].Computer Engineering and Applications,2003(13):208-211.(in Chinese)
[16] 黄名选,严小卫,张师超.基于文本库的完全加权词间关联规则挖掘算法[J].广西师范大学学报(自然科学版),2007,25(4):24-27. Huang Ming-xuan,Yan Xiao-wei, Zhang Shi-chao.Algorithm of item-all-weighted association rules mining between terms from text database[J].Journal of Guangxi Normal University(Natural Science Edition),2007,25(4):24-27.(in Chinese)
[17] 黄名选,严小卫,张师超.基于矩阵加权关联规则挖掘的伪相关反馈查询扩展[J].软件学报,2009,20(7):1854-1865. Huang Ming-xuan,Yan Xiao-wei,Zhang Shi-chao.Query expansion of pseudo relevance feedback based on matrix-weighted association rules mining[J].Journal of Software,2009,20(7):1854-1865.(in Chinese)
[18] 刘远超,王晓龙,徐志明,等.基于粗集理论的中文关键词短语构成规则挖掘[J].电子学报,2007,35(2):371-374. Liu Yuan-chao,Wang Xiao-long,Xu Zhi-ming,et al.Mining construction rules of chinese keyphrase based on rough set theory[J].Acta Electronic Sinica,2007,35(2):371-374.(in Chinese)
[19] Grossman D A,Frieder O.信息检索:算法与启发式方法[M].北京:人民邮电出版社,2010.12-13.
[20] Büttcher S,Clarke C L A,Cormack G V.信息检索实现和评价搜索引擎[M].北京:机械工业出版社,2012.40-41.
[21] 程继华,郭建生,施鹏飞.挖掘所关注规则的多策略方法研究[J].计算机学报,2000,23(1):47-51. Cheng Ji-hua,Guo Jian-sheng,Shi Peng-fei.Multi-strategy approach to mining interesting rules[J].Chinese Journal of Computers,2000,23(1):47-51.(in Chinese)
[22] 周欣,沙朝锋,朱扬勇,等.兴趣度—关联规则的又一个阈值[J].计算机研究与发展,2000,37(5):627-633. Zhou Xin,Sha Chao-feng,Zhu Yang-yong,et al.Interest measure--another threshold in association rules[J].Journal of Computer Research & Development,2000,37(5):627-633.(in Chinese)
2015, Vol. 43 
