Automatic item weight generation for pattern mining and its application

International Journal of Data Warehousing and Mining

Volumn 7, Issue 3, 2011, Pages 30-49

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

^a UNIVERSITY OF AUCKLAND   (New Zealand)

^b AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

Author keywords

Association rule mining; Item weighting; Principal components analysis; Valency model; Weighted association rule mining

Indexed keywords

ASSOCIATION RULE MINING; ITEM WEIGHTING; PRINCIPAL COMPONENTS ANALYSIS; VALENCIES; WEIGHTED ASSOCIATION RULES;

ASSOCIATION RULES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 80052829217     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2011070102     Document Type: Article

References (43)

1. Agrawal, R., Imielinski, T., & Swami, A. N. (1993). 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). New York, NY: ACM.
2. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.), Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). San Francisco, CA: Morgan Kaufmann Publishers.
3. Antonie, M.-L., & Zaiane, O. R. (2002). Text document categorization by term association. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM '02) (pp. 19-26). Washington, DC: IEEE Computer Society.
4. Ashrafi, M. Z., Taniar, D., & Smith, K. A. (2004). ODAM: An Optimized Distributed Association Rule Mining Algorithm. IEEE Distributed Systems Online, 5(3).
5. Ashrafi, M. Z., Taniar, D., & Smith, K. A. (2007). Redundant association rules reduction techniques. International Journal of Business Intelligence and Data Mining, 2(1), 29-63. doi:10.1504/IJBIDM.2007.012945
6. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. Retrieved from http://www.ics.uci.edu/-mlearn/MLRepository.html
7. Bookstein, A., Klein, S. T., & Raita, T. (1998). Clumping properties of content-bearing words. Journal of the American Society for Information Science American Society for Information Science, 49(2), 102-114. (Pubitemid 128594910)
8. Cai, C. H., Fu, A. W. C., Cheng, C. H., & Kwong, W. W. (1998). Mining association rules with weighted items. In Proceedings of the 1998 International Symposium on Database Engineering & Applications (IDEAS '98) (pp. 6877). Washington, DC: IEEE Computer Society.
9. Chen, X., & Wu, Y.-F. (2006). Personalized knowledge discovery, mining novel association rules from text. In Proceedings of the 6th SIAM International Conference on Data Mining (pp. 589-597). Bethesda, MD: Society of Industrial and Applied Mathematics.
10. Cohen, E., Datar, M., Fujiwara, S., Gionis, A., Indyk, P., & Motwani, R. (2001). Finding interesting association rules without support pruning. IEEE Transactions on Knowledge and Data Engineering, 13, 64-78. doi:10.1109/69.908981 (Pubitemid 32301044)
11. Daly, O., & Taniar, D. (2004). Exception Rules Mining Based on Negative Association Rules. In Proceedings of the International Conference on Computational Science and Its Applications (ICCSA 2004) (LNCS 3046, pp. 543-552). (Pubitemid 38761476)
12. Evans, D. A., & Lefferts, R. G. (1995). CLARITTREC experiments. In Proceedings of the 2nd Text Retrieval Conference (pp. 385-395). Elmsford, NY: Pergamon Press.
13. Faloutsos, C., Korn, F., Labrinidis, A., Labrinidis, R., Kotidis, Y., Perkovic, D., & Kaplunovich, A. (1997). Quantifiable data mining using principal component analysis (Tech. Rep. No. CS-TR-3754).
14. College Park, MD: Institute for Systems Research, University of Maryland.
15. Feldman, R., & Hirsh, H. (1996). Mining associations in text in the presence of background knowledge. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96) (pp. 343-346). Menlo Park, CA: AAAI Press.
16. Giannikopoulos, P., Varlamis, I., & Eirinaki, M. (2010). Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies. International Journal of Data Warehousing and Mining, 6(1), 58-76. doi:10.4018/jdwm.2010090804
17. Holt, J. D., & Chung, S. M. (2001). Multipass algorithms for mining association rules in text databases. Knowledge and Information Systems, 3(2), 168-183. doi:10.1007/PL00011664
18. Jian, W., & Ming, L. X. (2008). An effective mining algorithm for weighted association rules in communication networks. Journal of Computers, 3(4), 20-27.
19. Jiang, H., Zhao, Y., Yang, C., & Dong, X. (2008). Mining both positive and negative weighted association rules with multiple minimum supports. In Proceedings of the International Conference on Computer Science and Software Engineering (pp. 407-410). Washington, DC: IEEE Computer Society.
20. Jing, L.-P., Huang, H.-K., & Shi, H.-B. (2002). Improved feature selection approach TF-IDF in text mining. In Proceedings of the 2002 Conference on Machine Learning and Cybernetics (pp. 944-946).
21. Washington, DC: IEEE Computer Society. Koh, Y. S., Pears, R., & Yeap, W. (2010). Valency based weighted association rule mining. In M. Zaki, J. Yu, B. Ravindran, & V. Pudi (Eds.), Advances in Knowledge Discovery and Data Mining (LNCS 6118, pp. 274-285).
22. Koh, Y. S., & Rountree, N. (2005). Finding sporadic rules using apriori-inverse. In T. B. Ho, D. Cheung, & H. Liu (Eds.), Advances in Knowledge Discovery and Data Mining (LNCS 3518, pp. 97-106).
23. Koh, Y. S., Rountree, N., & O'Keefe, R. A. (2006). Finding Non-Coincidental Sporadic Rules Using Apriori-Inverse. International Journal of Data Warehousing and Mining, 2(2), 38-54. doi:10.4018/jdwm.2006040102
24. Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2003). Building text classifiers using positive and unlabeled examples. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM '03) (pp. 179-186). Washington, DC: IEEE Computer Society.
25. Liu, B., Hsu, W., & Ma, Y. (1999). 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). New York, NY: ACM.
26. Lopes, A., Pinho, R., Paulovich, F., & Minghim, R. (2007). Visual text mining using association rules. Computers & Graphics, 31(3), 316-326. doi:10.1016/j.cag.2007.01.023 (Pubitemid 46726585)
27. Qu, S., Wang, S., & Zou, Y. (2008). Improvement of text feature selection method based on TF-IDF. In Proceedings of the 2008 International Seminar on Future Information Technology and Management Engineering (FITME '08) (pp. 79-81). Washington, DC: IEEE Computer Society.
28. Ritchie, D. S. (2010). Students hit hard as swine flu clobbers schools. Retrieved October 20, 2010, from http://www.times-age.co.nz/local/news/students- hithard-as-swine-flu-clobbers-schools/3918707/
29. Sanjay, R., Ranka, S., & Tsur, S. (1997). Weighted association rules, Model and algorithm. Retrieved from http://citeseer.ist.psu.edu/185924.html
30. Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference (pp. 517-524). New York, NY: ACM.
31. Sun, K., & Bai, F. (2008). Mining weighted association rules without preassigned weights. IEEE Transactions on Knowledge and Data Engineering, 20(4), 489-495. doi:10.1109/TKDE.2007.190723 (Pubitemid 351337418)
32. Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Upper Saddle River, NJ: Pearson.
33. Taniar, D., Rahayu, W., Lee, V. C. S., & Daly, O. (2008). Exception rules in association rule mining. Applied Mathematics and Computation, 205(2), 735-750. doi:10.1016/j.amc.2008.05.020
34. Tao, F., Murtagh, F., & Farid, M. (2003). Weighted association rule mining using weighted support and significance framework. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 661-666). New York, NY: ACM.
35. Tjioe, H. C., & Taniar, D. (2005). Mining Association Rules in Data Warehouses. International Journal of Data Warehousing and Mining, 1(3), 28-62. doi:10.4018/jdwm.2005070103
36. Tzanis, G., & Berberidis, C. (2007). Mining for Mutually Exclusive Items in Transaction Databases. International Journal of Data Warehousing and Mining, 3(3), 45-59. doi:10.4018/jdwm.2007070104
37. Voters leave Australia hanging. (2010). Retrieved October 20, 2010, from http://www.abc.net.au/news/stories/2010/08/21/2989767.htm
38. Wang, W., Yang, J., & Yu, P. S. (2000). Efficient mining of weighted association rules (WAR). In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '00) (pp. 270-274). New York, NY: ACM.
39. Wong, P. C., Whitney, P., & Thomas, J. (1999). Visualizing association rules for text mining. In Proceedings of the IEEE Symposium on Information Visualization (pp. 120-123). Washington, DC: IEEE Computer Society. (Pubitemid 32213890)
40. Wu, S.-T., Li, Y., Xu, Y., Pham, B., & Chen, P. (2004). Automatic pattern-taxonomy extraction for web mining. In Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence (WI '04) (pp. 242-248). Washington, DC: IEEE Computer Society. (Pubitemid 40401107)
41. Yan, L., & Li, C. (2006). Incorporating pageview weight into an association-rule-based web recommendation system. In A. Sattar & B. H. Kang (Eds.), Proceedings of the Australian Conference on Artificial Intelligence (LNCS 4304, pp. 577-586).
42. Yu, H., Han, J., & Chang, K. C.-C. (2004). PEBL Web page classification without negative examples. IEEE Transactions on Knowledge and Data Engineering, 16(1), 70-81. doi:10.1109/TKDE.2004.1264823
43. Zhao, Y., Jiang, H., Geng, R., & Dong, X. (2009). Mining weighted negative association rules based on correlation from infrequent items. In Proceedings of the International Conference on Advanced Computer Control (pp. 270-273). Washington, DC: IEEE Computer Society.