Literature Review on Interestingness Based Data Mining for Business Development

Authors(2) :-Sakthi Nathiarasan A, Manikandan M

Association Rule mining is one of the popular data mining technique, which finds correlation between the data items present in the database. Normal Association Rule mining Algorithm finds frequent itemsets based on some statistical measures and it does not includes the interestingness of the business end user. This in turn leads to the development of Semantic Association Rule mining or utility mining. Utility mining considers external utility factors in addition to normal itemset frequencies. several utility mining Algorithms in the literature were discussed. In this paper we present a literature review of various research work carried by different researchers in the field of utility mining. Of course, a single article cannot be a complete review of all the research work, yet we hope that it will provide a guideline for future researches in utility mining.

Authors and Affiliations

Sakthi Nathiarasan A
Department of CSE, Adhiyamaan College of Engineering, Hosur, India
Manikandan M
Department of CSE, Adhiyamaan College of Engineering, Hosur, India

Association Rule Mining, Utility mining, Interestingness, Utility

[1] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int’l Conf. Very Large Data Bases (VLDB), pp. 487-499, 1994.
[2] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. 11th Int’l Conf. Data Eng., pp. 3-14, Mar. 1995.
[3] Yao,H. and Hamilton, H .J.,“Mining itemset utilities from transaction database”, Data & Knowledge Engineering, Elsevier journal Vol.59, pp.603-626, 2006.
[4] Ying Liu, Wei-keng Liao and Alok Choudhary, “A two-phase algorithm for fast discovery of high utility itemsets”. In 9th Pacific-Asia Conf. on Advances in Knowledge Discovery and Data Mining (PAKDD 2005), 3518, Springer-Verlag, Berlin, pp. 689–695, 2005.
[5] Jieh-shanyeh., Yu-Chiang Li and Chin -Chen Chang. “Two-Phase Algorithms for a Novel Utility-Frequent mining Model”, PAKDD Workshop, 2007, pp. 433-444.
[6] Shankar, S., Premalatha,k. and Kannimuthu,S. “iFUM– Improved Fast Utility Mining”,Vol.27, No.11,2011, pp.32-36.
[7] Shankar,S., Purusothaman,T., Jayanthi,S. and Nishanth Babu. “A Fast Algorithm for Mining high Utility Itemsets”, Proceedings of the IEEE International Advance Computing Conference (IACC 09), Patiala, India.
[8] Chun-Jung Chu, Vincent S. Tseng, Tyne Liang, “An efficient algorithm for mining temporal high utility itemsets from data streams”, Journal of System Software, Vol. 81, No. 7, 2008, pp. 1105-1117.
[9] B.-E. Shie, H.-F. Hsiao, V., S. Tseng, and P.S. Yu, “Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments,” Proc. 16th Int’l Conf. Database Systems for Advanced Applications (DASFAA ’11), vol. 6587/2011, pp. 224-238, 2011.
[10] H. Yao, H.J. Hamilton, and L. Geng, “A Unified Framework for Utility-Based Measures for Mining Itemsets,” Proc. ACM SIGKDD Second Workshop Utility-Based Data Mining, pp. 28-37, Aug. 2006.
[11] Mengchi Liu, Junfeng Qu “Mining High Utility Itemsets without Candidate Generation”.
[12] V.S. Tseng, C.W. Wu, B.E. Shie, and P.S. Yu, “UP-Growth: An Efficient Algorithm for High Utility Itemsets Mining,” Proc. 16th ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD’10), pp. 253-262, 2010.
[13] C.F. Ahmed, S.K. Tanbeer, B.S. Jeong, and Y.- K. Lee, “Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases,” IEEE Trans. Knowledge and Data Eng., vol. 21, no. 12, pp. 1708-1721, Dec. 2009.
[14] Vincent S. Tseng, Bai-En Shie, Cheng-Wei Wu, and Philip S. Yu “Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases”. IEEE transactions on knowledge and data engineering, vol. 25, no. 8, august 2013.
[15] Kenneth H. Rosen, "Discrete Mathematics and Its applications", Mc Graw Hill., 4th edition, 298-300.
[16] Vid Podpecan., Nada Lavrac. and Igor Kononenko.‘Fast Algorithm for Mining Utility Frequent Itemsets’,The Eleventh European Conference on Principles and Practice of Knowledge Discovery in Databases.,2009.
[17] Sakthi Nathiarasan A, Manikandan M, “Performance Oriented Mining of Utility Frequent Itemsets”, Proceedings of IEEE International Conference on Circuits, Communication, Control and Computing (I4C-2014), M S Ramaiah Institute of Technology, Bangalore, November 2014.
[18] Sakthi Nathiarasan A, “Genetic Algorithm Based Utility Mining for Finding High Utility Itemsets”, Proceedings of IEEE International conference on Advanced Computing(ICoAC-14), MIT Campus, Anna University, Chennai, December 2014.
[19] Sakthi Nathiarasan A, Kalaiyarasi, Manikandan, “Literature Review on Infrequent Itemset Mining Algorithms”, International Journal of a Advanced Research in Computer and Communication Engineering (IJARCCE), Vol 3, Issue 8, August 2014.

Publication Details

Published in : Volume 1 | Issue 1 | January-Febuary 2015
Date of Publication : 2015-02-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 99-104
Manuscript Number : IJSRSET151113
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Sakthi Nathiarasan A, Manikandan M, " Literature Review on Interestingness Based Data Mining for Business Development, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 1, pp.99-104, January-Febuary-2015.
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