FIUT: A new method for mining frequent itemsets

Information Sciences

Volumn 179, Issue 11, 2009, Pages 1724-1737

  Tsay, Yuh Jiuan ^a   Hsu, Tain Jung ^a   Yu, Jing Rung ^b  

^a NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL CHI NAN UNIVERSITY   (Taiwan)

Author keywords

FP growth; Frequent items ultrametric trees; Frequent itemsets; Ultrametric trees

Indexed keywords

ONLINE SEARCHING;

COMPUTING TIME; EFFICIENT METHODS; FP-GROWTH; FREQUENT ITEMS ULTRAMETRIC TREES; FREQUENT ITEMSETS; MINING FREQUENT ITEMSETS; SEARCH SPACES; SIMULATION RESULTS; ULTRAMETRIC TREES;

TREES (MATHEMATICS);

EID: 62249183038     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2009.01.010     Document Type: Article

References (21)

1. Agrawal R., Imielinski T., and Swami A. Data mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering 5 (1993) 914-925
2. R. Agrawal, R. Srikant, Fast algorithm for mining association rules in large databases, in: Proceedings of the 1994 International Conference VLDB, Santiago, Chile, 1994, pp. 487-499.
3. Agarwal R., Aggarwal C., and Prasad V.V.V. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing 61 (2001) 350-361
4. Berzal F., Cubero J.C., Marin N., and Serrano J.M. TBAR: An efficient method for association rule mining in relational databases. Data and Knowledge, Engineering 37 (2001) 47-64
5. S. Brin, R. Motwani, J.D. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data, in: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Tuscon, Arizona, 1997, pp. 255-264.
6. D. Cheung, J. Han, V. Ng, A. Fu, Y. Fu, A fast distributed algorithm for mining association rules, in: Proceedings of 1996 International Conference on Parallel and Distributed Information Systems, Miami Beach, Florida, 1996, pp. 31-44.
7. Dong J., and Han M. BitTableFI: an efficient mining frequent itemsets algorithm. Knowledge-Based Systems 20 (2007) 329-335
8. M. EI-Hajj, O.R. Zaiane, Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining, in: The ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 109-118.
9. J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, in: Proceedings of the 19th ACM SIGMOD International Conference on Management of Data, 2000, pp. 1-12.
10. Han J., and Kamber M. Data Mining Concepts and Techniques (2000), Morgan Kaufmann, San Francisco
11. Holt J.D., and Chung S.M. Mining association rules using inverted hashing and pruning. Information Processing Letter 83 (2002) 211-220
12. Hu T., Sung S.Y., Xiong H., and Fu Q. Discovery of maximum length frequent itemsets. Information Sciences 178 (2008) 68-87
13. Kantardzic M. DATA MINING: Concepts, Models, Methods, and Algorithms (2003), Wiley Inter-science, NJ
14. Liu G., Lu H., and Yu J.X. CFP-tree: a compact disk-based structure for storing and querying frequent itemsets. Information Sciences 32 (2007) 295-319
15. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, D. Yang, H-mine: hyper-structure mining of frequent patterns in large database, in: Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, 2001, pp. 441-448.
16. Parnas M., and Roh D. Testing metric properties. Information and Computation 187 (2003) 155-195
17. J.S. Park, M.S. Chen, P.S. Yu, An effective hash-based algorithm for mining association rules, in: Proceedings of 1995 ACM SIGMOD International Conference on Management of Data, San Jose, CA, 1995, pp. 175-186.
18. A. Savasere, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, in: Proceedings of the 21th VLDB Conference, Zurich, Switzerland, 1995, pp. 432-444.
19. Tsay Y.J., and Chang-Chien Y.W. An efficient cluster and decomposition algorithm for mining association rules. Information Sciences 160 22 (2004) 161-171
20. Tsay Y.J., and Chiang J.Y. CBAR: an efficient method for mining association rules. Knowledge-Based Systems 18 (2005) 99-105
21. Zhang S., Zhang J., and Zhang C. EDUA: an efficient algorithm for dynamic database mining. Information Sciences 177 (2007) 2756-2767