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http://dx.doi.org/10.9728/dcs.2016.17.2.81

Mining Association Rule on Service Data using Frequency and Weight  

Hwang, Jeong Hee (Namseoul University Computer Engineering)
Publication Information
Journal of Digital Contents Society / v.17, no.2, 2016 , pp. 81-88 More about this Journal
Abstract
The general frequent pattern mining considers frequency and support of items. To extract useful information, it is necessary to consider frequency and weight of items that reflects the changing of user interest as time passes. The suitable services considering time or location is requested by user so that the weighted mining method is necessary. We propose a method of weighted frequent pattern mining based on service ontology. The weight considering time and location is given to service items and it is applied to association rule mining method. The extracted rule is combined with stored service rule and it is based on timely service to offer for user.
Keywords
Association rule; Data mining; Frequent pattern; Ontology;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 D. Han, D. Kim, J. Kim, C. Na, B. Hwang, "A Method for Mining Interval Event Association Rules from a Set of Events Having Time Property," Journal of Korea Information Processing Society, Vol.16-D, No.2, pp.186-190, 2009
2 U. Yun, J. J. Leggett, "WIP:mining Weighted Interesting Patterns with a strong weight and/or support affinity," SIAM International Conference on Data Mining, pp. 624-628, 2006
3 H. Yun, D. Ha, B. Hwang, K. Ryu, "Mining Association Rules on Significant Rare Data Using Relative Support," Journal of Systems and Software, Vol.67, No.3, pp.181-191, 2003   DOI
4 R. J. Swargam, and M. J. Palakal, "The Role of Least Frequent Item Sets in Association Discovery," In Proc. of International Conference on Digital Information Management, 2007
5 C. F. Ahmed, S. K. Tanbeer, B. S. Jeong, Y. K Lee, "Mining Weighted Frequent Patterns in Incremental Databases," Proc. of the Pacific Rim, 2008
6 F. Tao, "Weighted Association Rule Mining using Weighted Support and Significant Framework," Proc. of the ACM SIGKDD, 2003
7 W. Wang, J. Yang, P. S. Yu, "WAR:Weighted Association Rules for Item Intensities," Knowledge Information and Systems, 2004   DOI
8 U. Yun, J. J. Leggett, "WFIM:Weighted Frequent Itemset Mining with a Weight Range and a Minimum Weight," Proc. of the Fourth SIAM Int. Conf. on Data Mining, 2005
9 S. Lo, "Binary Prediction based on Weighted Sequential Mining Method," Proc. of the Int'l Conf. on Web Intelligence, pp.755-761, 2005
10 U. Yun, "A New Framework for Detecting Weighted Sequential Patterns in Large Sequential Databases," Knowledge-Based Systems, 2008   DOI
11 R. S. Thakur, R.C. Jain and K. R. Pardasani, "Mining Level-Crossing Association Rules from Large Databases," Journal of Computer Science 2(1), pp. 76-81, 2006.   DOI
12 V. Ramana, M. Rathnamma, A. Reddy, "Methods for Mining Cross Level Association Rule In Taxonomy Data Structures," International Journal of Computer Applications, Vol. 7, No. 3, 2010.