Valency based weighted association rule mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6118 LNAI, Issue PART 1, 2010, Pages 274-285

  Koh, Yun Sing ^a   Pears, Russel ^a   Yeap, Wai ^a  

^a AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

Author keywords

Principal components; Valency; Weighted association rule mining

Indexed keywords

ASSOCIATION RULE MINING; DATA MINING TASKS; DATA SETS; NATURAL VARIATION; PRINCIPAL COMPONENTS; TRANSACTION DATABASE; UNIFORM DISTRIBUTION; VALENCIES; WEIGHTED ASSOCIATION RULE MINING; WEIGHTED ASSOCIATION RULES; WEIGHTING SCHEME;

ASSOCIATION RULES;

DATA MINING;

EID: 79956303331     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13657-3_31     Document Type: Conference Paper

References (16)

1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207-216 (1993)
2. Cai, C.H., Fu, A.W.C., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: IDEAS 1998: Proceedings of the 1998 International Symposium on Database Engineering & Applications, Washington, DC, USA, p. 68, IEEE Computer Society, Los Alamitos (1998)
3. Sun, K., Bai, F.: Mining weighted association rules without preassigned weights. IEEE Trans. on Knowl. and Data Eng. 20(4), 489-495 (2008) (Pubitemid 351337418)
4. Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: KDD 2000: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 270-274. ACM, New York (2000)
5. Yan, L., Li, C.: Incorporating pageview weight into an association-rule-based web recommendation system. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 577-586. Springer, Heidelberg (2006)
6. Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337-341 (1999)
7. Koh, Y.S., Rountree, N.: Finding sporadic rules using apriori-inverse. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 97-106. Springer, Heidelberg (2005)
8. Cohen, E.,Datar,M., Fujiwara, S.,Gionis, A., Indyk, P.,Motwani, R., Ullman, J.D.,Yang, C.: Finding interesting association rules without support pruning. IEEE Transactions on Knowledge and Data Engineering 13, 64-78 (2001) (Pubitemid 32301044)
9. Sanjay, R., Ranka, S., Tsur, S.: Weighted association rules: Model and algorithm (1997), http://citeseer.ist.psu.edu/185924.html
10. Tao, F., Murtagh, F., Farid, M.: Weighted association rule mining using weighted support and significance framework. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 661-666. ACM, New York (2003)
11. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago, Chile, pp. 487-499 (1994)
12. Jian, W., Ming, L.X.: An effective mining algorithm for weighted association rules in communication networks. Journal of Computers 3(4) (2008)
13. Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference, pp. 517-524. ACM, New York (1968)
14. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Inc., London (2006)
15. Faloutsos, C., Korn, F.A., Labrinidis, Y., Kotidis, A.K., Perkovic, D.: Quantifiable data mining using principal component analysis. Technical Report CS-TR-3754, Institute for Systems Research, University of Maryland, College Park, MD (1997)
16. Asuncion, A., Newman, D.: UCI machine learning repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html