INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.8, No.7, Jul. 2016

On the Use of Rough Set Theory for Mining Periodic Frequent Patterns

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Author(s)

Manjeet Samoliya, Akhilesh Tiwari

Index Terms

Association rule mining;frequent pattern;periodic pattern mining;rough sets;temporal database

Abstract

This paper presents a new Apriori based approach for mining periodic frequent patterns from the temporal database. The proposed approach utilizes the concept of rough set theory for obtaining reduced representation of the initially considered temporal database. In order to consider only the relevant items for analyzing seasonal effects, a decision attribute festival has been considered. It has been observed that the proposed approach works fine for the analysis of the seasonal impact on buying behavior of customers. Considering the capability of approach for the analysis of seasonal profitability concern, decision making, and future marketing may use it for the important decision-making process for the uplifting of sell.

Cite This Paper

Manjeet Samoliya, Akhilesh Tiwari,"On the Use of Rough Set Theory for Mining Periodic Frequent Patterns", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.7, pp.53-60, 2016. DOI: 10.5815/ijitcs.2016.07.08

Reference

[1]J. Han and M. Kamber , “Data Mining: Concepts and Techniques”, 2nd ed.,The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor 2006.

[2]U. M. Fayyad, G. P. Shapiro and P. Smyth, From Data Mining to Knowledge Discovery in Databases. 0738-4602-1996, AI Magazine (Fall 1996).pp: 37–53.

[3]J. Han and M. Kamber, Data Mining: Concepts and Techniques. Second edition Morgan Kaufmann Publishers.

[4]RakeshAgrawal, SaktiGhosh, Tomasz Imielinski, BalaIyer, and Arun Swami, An Interval Classier for Database Mining Applications", VLDB-92 , Vancouver, British Columbia, 1992, 560-573.

[5]D. Heckerman, H. Mannila, D. Pregibon, and R. Uthurusamy, editors. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97).AAAI Press, 1997.

[6]R. Slowinski, J. Stefanowski, Rough Classification with Valued Closeness Relation, in: E. Diday, Y. Lechevallier, M. Schader, P. Bertrand, B. Burtschy_Eds.., New Approaches in Classification and Data Analysis, Springer, Berlin, 1994.

[7]E. Simoudis, J. W. Han, and U. Fayyad, editors.Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96).AAAI Press, 1996.

[8]Y. Maeda, K. Senoo, H. Tanaka: Interval density function in conflict analysis, in: N. Zhong, A. Skowron, S. Ohsuga, (eds.), New Directions in Rough Sets, Data Mining and Granular-Soft Computing, Springer, 1999, 382-389.

[9]J. Yang, W. Wang, and P.S. Yu.Mining asynchronous periodic patterns in time series data. IEEE Transaction on Knowledge and Data Engineering, 15(3):613-628, 2003.

[10]Omari et.al. new temporal measure for association rule mining. Second International Conference on Knowledge Discovery and Data Mining,1997.

[11]Chen Chu-xiang, Shen Jiang-jing, Chen Bing, et al., “An Improvement Apriori Arithmetic Based on Rough Set Theory”, In proceeding of the 2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS). pp.1-3, 2011.

[12]B. Ozden, S. Ramaswamy, and A. Silberschatz.Cyclic association rules. In Proc. of the 14th InternationalConference on Data Engineering,, pages 412–421, 1998.

[13]J. Han, G. Dong, and Y. Yin.Efficient mining paritial periodic patterns in time series database. In Proc. of the15th International Conference on Data Engineering,,pages 106–115, 1999.

[14]Agrawal, R., Imielinski, T., and Swami, A. N. 1993. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216.

[15]Dixit et al.,”A Survey of Various Association Rule Mining Approaches” International Journal of Advanced Research in Computer Science and Software Engineering 4(3), March - 2014, pp. 651-655.

[16]Pradnya A. Shirsath, Vijay Kumar Verma, “A Recent Survey on Incremental Temporal Association Rule Mining”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-1, June 2013.

[17]Yiyu Yao, “Rough Set Approximations: A Concept Analysis Point Of View”, University of Regina, Regina, Saskatchewan, Canada, 2015.

[18]En-Bing Lin and Yu-RuSyau, “Comparisons between Rough Set Based and Computational Applications in Data Mining”, International Journal of Machine Learning and Computing, Vol. 4, No. 4, August 2014.

[19]R. Raghavan and B.K.Tripathy,” On Some Comparison Properties of Rough Sets Based on Multigranulations and Types of Multigranular Approximations of Classifications”, I.J. Intelligent Systems and Applications, 2013, 06, 70-77.

[20]Xuan Thao Nguyen, Van Dinh Nguyen and Doan Dong Nguyen,” Rough Fuzzy Relation on Two Universal Sets”, I.J. Intelligent Systems and Applications, 2014, 04, 49-55.

[21]K. Lavanya, N.Ch.S.N. Iyengar, M.A. Saleem Durai and T. Raguchander, “Rough Set Model for Nutrition Management in Site Specific Rice Growing Areas”, I.J. Intelligent Systems and Applications, 2014, 10, 77-86.

[22]Essam Al Daoud, “An Efficient Algorithm for Finding a Fuzzy Rough Set Reduct Using an Improved Harmony Search”, I.J. Modern Education and Computer Science, 2015, 2, 16-23.