Weighted association rule mining via a graph based connectivity model

Information Sciences

Volumn 218, Issue , 2013, Pages 61-84

  Pears, Russel ^a   Koh, Yun Sing ^b   Dobbie, Gillian ^b   Yeap, Wai ^a  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

^b UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

Association rule mining; Connectivity; Graph cliques; Item weights; Purity; Valency model

Indexed keywords

CONNECTIVITY; GRAPH CLIQUES; ITEM WEIGHTS; PURITY; VALENCIES;

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

DATA MINING;

EID: 84867405323     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.07.001     Document Type: Article

References (26)

1. R. Agrawal, T. Imielinski, A.N. Swami, Mining association rules between sets of items in large databases, in: P. Buneman, S. Jajodia (Eds.), Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216.
2. C.F. Ahmed, S.K. Tanbeer, B.S. Jeong, and H.J. Choi A framework for mining interesting high utility patterns with a strong frequency affinity Information Sciences 181 21 2011 4878 4894
3. Y.M. Alexander Nanopoulos, and Apostolos N Papadopoulos Mining association rules in very large clustered domains Elsevier Information Systems 32 2007 649 669
4. Y. Bengio, O. Delalleau, and N. Le Roux Label propagation and quadratic criterion Semi-Supervised Learning 2006 MIT Press
5. C.H. Cai, A.W.C. Fu, C.H. Cheng, and W.W. Kwong Mining association rules with weighted items IDEAS '98: Proceedings of the 1998 International Symposium on Database Engineering & Applications 1998 IEEE Computer Society Washington, DC, USA 68
6. E. Cohen, M. Datar, S. Fujiwara, A. Gionis, P. Indyk, R. Motwani, J.D. Ullman, and C. Yang Finding interesting association rules without support pruning IEEE Transactions on Knowledge and Data Engineering 13 2001 64 78
7. L.C. Freeman Centrality in social networks: conceptual clarification Social Networks 1 3 1979 215 239
8. Y.S. Koh, R. Pears, and G. Dobbie Automatic item weight generation for pattern mining and its application International Journal of Data Warehousing and Data Mining 7 3 2011 30 49
9. Y.S. Koh, R. Pears, and W. Yeap Valency based weighted association rule mining The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining 2010 Springer
10. Y.S. Koh, and N. Rountree Finding sporadic rules using Apriori-inverse D.C.T.B. Ho, H. Liu, Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia Conference, PAKDD 2005, Hanoi, Vietnam Lecture Notes in Computer Science vol. 3518 2005 Springer 97 106
11. T. Li, and X. Li Novel alarm correlation analysis system based on association rules mining in telecommunication networks Information Sciences 180 16 2010 2960 2978
12. S. Lu, H. Hu, and F. Li Mining weighted association rules Intelligent Data Analysis 5 2001 211 225
13. R. Michalski Learning by being told and learning from examples: an experimental comparison of the two methodes of knowledge acquisition in the context of developing an expert system for soybean desease diagnoiss International Journal of Policy Analysis and Information Systems 4 1980 125 161
14. R.T. Ng, L.V.S. Lakshmanan, J. Han, and A. Pang Exploratory mining and pruning optimizations of constrained associations rules SIGMOD Record 27 1998 13 24
15. J. Pei, J. Han, and W. Wang Constraint-based sequential pattern mining: the pattern-growth methods Journal of Intelligent Information Systems 28 2007 133 160
16. R. Sanjay, S. Ranka, S. Tsur, Weighted Association Rules: Model and Algorithm, 1997. < http://citeseer.ist.psu.edu/185924.html >.
17. D. Shepard A two-dimensional interpolation function for irregularly-spaced data Proceedings of the 1968 23rd ACM National Conference 1968 ACM 517 524
18. K. Sun, and F. Bai Mining weighted association rules without preassigned weights IEEE Transactions on Knowledge and Data Engineering 20 2008 489 495
19. F. Tao, F. Murtagh, and M. Farid Weighted association rule mining using weighted support and significance framework KDD '03: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2003 ACM New York, NY, USA 661 666
20. R. Tewarson On the gaussian elimination method for inverting sparse matrices Computing 9 1972 1 7
21. M. Wang, X.S. Hua, R. Hong, J. Tang, G.J. Qi, and Y. Song Unified video annotation via multigraph learning IEEE Transaction on Circuit and System for Video Technology 19 5 2009 733 746
22. M. Wang, X.S. Hua, J. Tang, and R. Hong Beyond distance measurement: constructing neighborhood similarity for video annotation IEEE Transactions on Multimedia 11 2009 465 476
23. W. Wang, J. Yang, and P.S. Yu Efficient mining of weighted association rules (WAR) KDD '00: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2000 ACM New York, NY, USA 270 274
24. H. Xiong, P.N. Tan, and V. Kumar Mining strong affinity association patterns in data sets with skewed support distribution ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining 2003 IEEE Computer Society Washington, DC, USA 387
25. L. Yan, and C. Li Incorporating pageview weight into an association-rule-based web recommendation system A. Sattar, B.H. Kang, Australian Conference on Artificial Intelligence Lecture Notes in Computer Science vol. 4304 2006 Springer 577 586
26. U. Yun Efficient mining of weighted interesting patterns with a strong weight and/or support affinity Information Sciences 177 17 2007 3477 3499