A Study of Negative Association Rules Mining Algorithm Based on Multi-Database

Abstract:

the mutual exclusion relationships among data items are reflected by negative association rules, whitch is very important on the decision-making analysis. In the last several years, negative association rules are frequently researched, while the study object of it is single mining of database now. With the development of database technology, multi-database mining is more and more important. On the basis of analyzing the related technology, research status and shortage of present negative association rules mining , the selecting rules, weighted synthesis and algorithm are discussed on multi-datobase.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3435-3439
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3435
• Online since: September 2013
• Authors: Xu Shan Peng, Ping Cheng, Mo Gei Wang
• Keywords: Data Mining (DM), Multi-Database, Negative Association Rules

References

[1] Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Proc of ACM SIGMOD Int Conf Management of Date [C]. Washington D C.

DOI: 10.1145/170035.170072

[2] Xushan Peng, Yanyan Wu. The Research and Application of Algorithm for Mining Positive Association Rules. 2011 International Conference on Electronic and Mechanical Engineering and Information Technology, 2011442 9-4431.

DOI: 10.1109/emeit.2011.6024017

[3] Brin S, Motwani R, Silverstein cristiano Beyond market: Generalizing association rules to correlations [A]. Processing of the ACM SIGMOD Conferrence 1997 [m]. New York: ACM Press, 1997. 265-276.

DOI: 10.1145/253262.253327

[4] Savasere A, Omiecinski E, Navathe S. M ining for strong negative associations in A large database of customer transaction [C] / / Proceeding of the IEEE 14 th Int Conference on Data Engineering losAlamitos: IEEE - CS, 1998, : 494-502.

DOI: 10.1109/icde.1998.655812

[5] LuXueYan, wang yong, ZhouYongQuan. A bit matrix based on the negative association rules mining new algorithm [J]. Journal of guangxi university for nationalities (natural science edition), 2007, (4) : 57-60.

[6] DongXiangJun, SongHan Bristol, ginger together, et al. Based on the minimum degree of interest in the positive and negative association rules mining [J]. Computer engineering and application, 2004, 27: 24-31.

[7] zhang qian, WangZhi and, ZhangGuoZhi. Based on the correlation coefficient of the positive and negative association rules mining algorithm [J]. Journal of shaanxi institute of science and technology, 2005, (4) : 35-38.

[8] GuoYueBin, ZhaiYanFu, DongXiangJun et al. Based on sequence model of positive and negative association rules [J]. Journal of shandong university (physical edition), September 2007 (9) : 88-95.

[9] DongXiangJun, WangShuJing, SongHan et al. Negative association rules [J]. Journal of Beijing university of science and technology, 2004, (11) : 78-81.

[10] SuYiJuan, YanXiaoWei. An improved algorithm for mining frequent set [J]. Journal of guangxi normal university (natural science edition), 2001, 12 (3) : 22-26.

[11] TangYiFang, cow dint, zhang t ultra. Data mining of association rules algorithm [J]. Journal of guangxi normal university (natural science edition), 2002, 16 (4) : 27-31.