An improved data mining approach using predictive itemsets

Expert Systems with Applications

Volumn 36, Issue 1, 2009, Pages 72-80

  Hong, Tzung Pei ^a   Horng, Chyan Yuan ^b   Wu, Chih Hung ^a   Wang, Shyue Liang ^a  

^a NATIONAL UNIVERSITY OF KAOHSIUNG   (Taiwan)

^b I SHOU UNIVERSITY   (Taiwan)

Author keywords

Association rule; Data dependency; Data mining; Predicting minimum support; Predictive itemset

Indexed keywords

BOOLEAN FUNCTIONS; DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; MINING;

ASSOCIATION RULE; DATA DEPENDENCY; PREDICTING MINIMUM SUPPORT; PREDICTIVE ITEMSET;

DATA MINING;

EID: 53849131698     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.09.009     Document Type: Article

References (23)

1. Agrawal R., Imielinksi T., and Swami A. Dataset mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 6 (1993) 914-925
2. Agrawal, R., Imielinksi, T., & Swami, A. (1993b). Mining association rules between sets of items in large dataset. In The ACM SIGMOD conference (pp. 207-216). Washington DC, USA.
3. Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules. In The international conference on very large data bases (pp. 487-499).
4. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In The 11th IEEE international conference on data engineering (pp. 3-14).
5. Agrawal, R., Srikant, R., & Vu, Q. (1997). Mining association rules with item constraints. In The third international conference on knowledge discovery in datasets and data mining (pp. 67-73). Newport Beach, California.
6. Bastide Y., Taouil R., Pasquier N., Stumme G., and Lakhal L. Mining frequent patterns with counting inference. ACM SIGKDD Explorations 2 2 (2000) 66-75
7. Bykowski, A., & Rigotti, C. (2001). A condensed representation to find frequent patterns. In The 12th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems. Santa Barbara, California, USA.
8. Chen M.S., Han J., and Yu P.S. Data mining: An overview from a dataset perspective. IEEE Transactions on Knowledge and Data Engineering 8 6 (1996) 866-883
9. Denwattana, N., & Getta, J. R. (2001). A parameterised algorithm for mining association rules. In The 12th Australasian Dataset conference (pp. 45-51).
10. Ezeife, C. I. (2002). Mining Incremental association rules with generalized FP-tree. In The 15th conference of the Canadian society for computational studies of intelligence on advances in artificial intelligence (pp. 147-160).
11. Han, J., & Fu, Y. (1995). Discovery of multiple-level association rules from large dataset. In The 21st international conference on very large data bases (pp. 420-431). Zurich, Switzerland.
12. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In The 2000 ACM SIGMOD international conference on management of data (pp. 1-12).
13. IBM, The Intelligent Information Systems Research (Quest) Group, http://www.almaden.ibm.com/software/quest/Resources/datasets/syndata.html.
14. Koh, J. L., & Shieh, S. F. (2004). An efficient approach for maintaining association rules based on adjusting FP-tree structures. In The 9th international conference on database systems for advanced applications (pp. 417-424).
15. Mannila, H., Toivonen, H., & Verkamo, A. I. (1994). Efficient algorithm for discovering association rules. The AAAI Workshop on Knowledge Discovery in Datasets (pp. 181-192).
16. Park J.S., Chen M.S., and Yu P.S. Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering 9 5 (1997) 812-825
17. Pei, J., Han, J., & Mao, R. (2000). CLOSET: an efficient algorithm for mining frequent closed itemsets. The 2000 ACM SIGMOD DMKD'00. Dallas, TX, USA.
18. Qiu, Y., Lan, Y. J., & Xie, Q. S. (2004). An improved algorithm of mining from FP-tree. In The third international conference on machine learning and cybernetics (pp. 26-29).
19. Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In The 21st international conference on very large data bases (pp. 407-419), Zurich, Switzerland.
20. Srikant, R., & Agrawal, R. (1996). Mining quantitative association rules in large relational tables. In The 1996 ACM SIGMOD international conference on management of data (pp. 1-12). Montreal, Canada.
21. Wojciechowski, M., & Zakrzewicz, M. (2002). Dataset filtering techniques in constraint-based frequent pattern mining. Pattern detection and discovery. London, UK.
22. Zaiane, O. R., & Mohammed, E. H. (2003). COFI-tree mining: A new approach to pattern growth with reduced candidacy generation. In The IEEE international conference on data mining.
23. Zaki, M. J., Hsiao, & C. J. (2002). CHARM: an efficient algorithm for closed itemset mining. In The second SIAM international conference on data mining. Arlington.