An effective mining approach for up-to-date patterns

Expert Systems with Applications

Volumn 36, Issue 6, 2009, Pages 9747-9752

  Hong, Tzung Pei ^a,b   Wu, Yi Ying ^a   Wang, Shyue Liang ^a  

^a NATIONAL UNIVERSITY OF KAOHSIUNG   (Taiwan)

^b NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

Author keywords

Data mining; Lifetime; Temporal patterns; Up to date patterns

Indexed keywords

ITEM SETS; ITEMSET; KNOWLEDGE DISCOVERY FROM DATABASE; LIFETIME; MINING ASSOCIATION RULES; NEW CONCEPTS; SUPPORT THRESHOLDS; TEMPORAL DATA MININGS; TEMPORAL DATUM; TEMPORAL PATTERNS; UP-TO-DATE PATTERNS;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; KNOWLEDGE MANAGEMENT;

MINING;

EID: 64049088807     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.02.029     Document Type: Article

References (18)

1. Agrawal, R., Imielinksi, T., & Swami, A. (1993a). Mining association rules between sets of items in large database. In The ACM SIGMOD conference, Washington, DC, USA.
2. Agrawal R., Imielinksi T., and Swami A. Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 6 (1993) 914-925
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 7th international conference on data engineering (pp. 3-14).
5. Agrawal, R., Srikant, R., & Vu, Q. (1997). Mining association rules with item constraints. In The 3rd international conference on knowledge discovery in databases and data mining, Newport Beach, California.
6. Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In The ACM SIGMOD conference, Tucson, Arizona, USA (pp. 255-264).
7. Frawley, W. J., Shapiro, G. P., & Matheus, C. J. (1991). Knowledge discovery in databases: An overview. In The AAAI workshop on knowledge discovery in databases (pp.1-27).
8. Han, J., & Fu, Y. (1995). Discovery of multiple-level association rules from large database. In The 21st international conference on very large data bases, Zurich, Switzerland (pp. 420-431).
9. Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In ACM SIGMOD conference (pp. 1-12).
10. Li D., and Deogun J.S. Discovering partial periodic sequential association rules with time lag in multiple sequences for prediction. Lecture Notes in Computer Sciences 3488 (2005) 332-341
11. Li Y., Ning P., Wang X.S., and Jajodia S. Discovering calendar-based temporal association rules. Data and Knowledge Engineering (2003) 193-218
12. Mannila, H., Toivonen, H., & Verkamo, I.A. (1994). Efficient algorithm for discovering association rules. In The AAAI workshop knowledge discovery in databases (pp. 181-192).
13. Ozden, B., Ramaswamy, S., & Silberschatz, A. (1998). Cyclic association rules. In The 14th international conference on data engineering, Orlando, Florida, USA (pp. 12-421).
14. 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 (1997) 812-825
15. Roddick J.F., and Spiliopoulou M. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering (2002) 750-767
16. Savasere, A., Omiecinski, E., & Navathe, S. (1995). An efficient algorithm for mining association rules in large databases. In The ACM VLDB conference (pp. 432-444).
17. Srikant, R., & Agrawal, R. (1995). Mining generalized association rules. In The 21st international conference on very large data bases, Zurich, Switzerland (pp. 407-419).
18. Verma K., Vyas O.P., and Vyas R. Temporal approach to association rule mining using T-tree and P-tree. Lecture Notes in Computer Science 3587 (2005) 651-659