International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 8, No. 3, 8 Mar. 2016
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Rough Set and Genetic Based Approach for Max-imization of Weighted Association Rules
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Author(s)
Index Terms
Weighted items, Rough Set Theory, Apriori Algorithm, min. w-support, min. w-confidence, weighted association rule mining, the Genetic Algorithm
Abstract
The present paper proposes a new approach for the effective weighted association rule mining. The proposed approach utilizes the power of Rough Set Theory for obtaining reduct of the targeted dataset. Additionally, approach takes the benefit for weighted measures and the Genetic Algorithm for the generation of the desired set of rules. Enough analysis of proposed approach has been done and observed that the approach works as per the expectation and will be beneficial in situation when there is a requirement for the consideration of hidden rules(maximizing generated rules) in decision-making process.
Cite This Paper
Shrikant Brajesh Sagar, Akhilesh Tiwari, "Rough Set and Genetic Based Approach for Maximization of Weighted Association Rules", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.3, pp.54-63, 2016. DOI:10.5815/ijmecs.2016.03.07
Reference
[1]R.Agrawal, T. Imielinski, and A.Swami, “Mining asso-ciation rules between sets of items in large databases”. In the Proc. of the ACM SIGMOD Int'l Cod, on Man-agement of Data (ACM SIGMOD '93), Washington, USA, May 1993.
[2]Agrawal R and Srikant R (1994) “Fast algorithms for Mining association rules”. In Proceedings of the 20th VLDB Conference, pages 487-499, 1994.
[3]Darshan M. Tank, “Improved Apriori Algorithm for Mining Association Rules”, I.J. Information Technolo-gy and Computer Science, 2014, 07, 15-23, MECS, June 2014.
[4]WANG Guo-Yin, YAO Yi-Yu, YU Hong, “A Survey
on Rough Set Theory and Applications” [J]. CHINESE JOURNAL OF COMPUTERS, 2009, 32(7): 1230-1246.
[5]Zdzisław Pawlak, “Rough Set Theory and its applica-tion” Journal of Telecommunication and Information Technology, 2012.
[6]T. Nishino, M. Nagamachi, H. Tanaka, “Variable preci-sion Bayesian rough set model and its application to human evaluation data”, in: RSFDGrC, Lecture Notes in Artificial Intelligence, vol. 3641, Springer-Verlag, Berlin, 2005.
[7]Z. Pawlak, S. Andrzej, “Rough sets and Boolean rea-soning”, Inform. Sci. 177 (1) (2007) 41–73.
[8]P. Pattaraintakorn, N. Cercone, K. Naruedomkul, “Rule learning: ordinal prediction based on rough sets and soft-computing”, Appl. Math. Lett 19 (12) (2006) 1300–1307.
[9]Chen Chu-xiang, Shen Jian-jing, Chen Bing, Shang Chang-xing, Wang Yun-cheng, “An Improvement Apriori Arithmetic based on Rough Set Theory” IEEE 2011.
[10]XUN Jiao, XULian-cheng, QILin, “Association Rules Mining Algorithm Based on Rough Set” IEEE Interna-tional symposium on information technology in medi-cine and education, 2012.
[11]Aritra Roy, Rajdeep Chatterjee, “Introducing New Hy-brid Rough Fuzzy Association Rule Mining Algorithm” ACEEE, Proc. of Int. Conf. on Recent Trends in Infor-mation, Telecommunication, and Computing, ITC, 2014.
[12]Cai, et al., "Mining Association Rules with Weighted Items", Database Engineering and Applications Sympo-sium, 1998, In Proceedings of IDEAS '98.
[13]Lu, et al., "Mining weighted association rules", Intelli-gent Data Analysis 5, pp.211-225, 2001.
[14]Feng Tao, Fionn Murtagh, Mohsen Farid, “Weighted Association Rule Mining using Weighted Support and Significance Framework” SIGKDD 2003.
[15]Luca Cagliero and Paolo Garza, "Infrequent Weighted Itemset Mining Using Frequent Pattern Growth", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 4, 2014.
[16]M. Sulaiman Khan, Maybin Muyeba, and Frans Coenen, “Weighted Association Rule Mining from Binary and Fuzzy Data” pringer-Verlag Berlin Heidelberg, LNAI 5077, pp. 200–212, 2008.
[17]Fu Jinghonga1, Zhang Chunyinga, Wang Jinga, Tian Fang, “A Weighted Relational Classification Algorithm Based on Rough Set”, I.J. Education and Management Engineering, 2013, 2, 20-19, MECS, February 2013.
[18]Preetham Kumar, Ananthanarayana V S, “Discovery of Weighted Association Rules Mining”, IEEE, 2010.
[19]Ke Sun and Fengshan Bai, “Mining Weighted Associa-tion Rules without Preassigned Weights” Knowledge and Data Engineering, IEEE Transactions on (Vol-ume:20 , Issue: 4 ), Page(s): 489-495, 2008.
[20]Alex A. Freitas, "A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery" Postgradu-ate Program in Computer Science, Pontilicia Univer-sidade Catolica do Parana Rna Imaculada Conceicao, 1155. Curitiba PR. 80215-901. Brazil.
[21]Manish Saggar, Ashish Kumar, Agrawal Abhimanyu Lad, “Optimization of Association Rule Mining using Improved Genetic Algorithms” International Confer-ence on Systems, Man and Cybernetics, IEEE 2004.
[22]Mohit K. Gupta and Geeta Sikka “Association Rules Extraction using Multi-objective Feature of Genetic Al-gorithm”, Proceedings of the World Congress on Engi-neering and Computer Science 2013 Vol. II.