A fast algorithm for mining share-frequent itemsets

Lecture Notes in Computer Science

Volumn 3399, Issue , 2005, Pages 417-428

  Li, Yu Chiang ^a   Yeh, Jieh Shan ^b   Chang, Chin Chen ^a,c  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^b PROVIDENCE UNIVERSITY   (Taiwan)

^c FENG CHIA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATABASE SYSTEMS;

DOWNWARD CLOSURE PROPERTIES; FAST SHARE MEASURE (FSM) ALGORITHM; ITEMSETS; MINING ASSOCIATION RULES;

DATA MINING;

EID: 24144440983     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-31849-1_41     Document Type: Conference Paper

References (19)

1. R. C. Agarwal, C. C. Aggarwal, and V. V. V. Prasad: A Tree Projection Algorithm for Generation of Frequent Itemsets. Journal of Parallel and Distributed Computing 61 (2001) 350-361
2. R. Agrawal, T. Imielinski, and A. Swami: Mining Association Rules between Sets of Items in Large Databases. In: Proc. 1993 ACM SIGMOD Intl. Conf. on Management of Data, Washington, D.C., (1993) 207-216
3. R. Agrawal and R. Srikant: Fast Algorithms for Mining Association Rules. In Proc. 20th Intl. Conf. on Very Large Data Bases, Santiago, Chile (1994) 487-499
4. B. Barber and H. J. Hamilton: Algorithms for Mining Share Frequent Itemsets Containing Infrequent Subsets. In: D. A. Zighed, H. J. Komorowski, J. M. Zytkow (eds.): Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Sciences, Vol. 1910. Springer-Verlag, Berlin Heidelberg New York (2000) 316-324
5. B. Barber and H. J. Hamilton: Parametric Algorithm for Mining Share Frequent Itemsets. Journal of Intelligent Information Systems 16 (2001) 277-293
6. B. Barber and H. J. Hamilton: Extracting Share Frequent Itemsets with Infrequent Subsets. Data Mining and Knowledge Discovery 7 (2003) 153-185
7. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur: Dynamic Itemset Counting and Implication Rules for Market Basket Data. In: Proc. 1997 ACM SIGMOD Intl. Conf. on Management of Data, Tucson, AZ (1997) 255-264
8. F. Berzal, J. C. Cubero, N. Marín, and J. M. Serrano: TBAR: An Efficient Method for Association Rule Mining in Relational Databases. Data & Knowledge Engineering 37 (2001) 47-64
9. C. L. Carter, H. J. Hamilton, and N. Cercone: Share Based Measures for Itemsets. In: H. J. Komorowski, J. M. Zytkow (eds.): Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science, Vol. 1263. Springer-Verlag, Berlin Heidelberg New York (1997) 14-24
10. M. S. Chen, J. Han, and P. S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowledge Data Engineering 8 (1996) 866-883
11. G. Grahne and J. Zhu: Efficient using Prefix-Tree in Mining Frequent Itemsets. In: Proc. IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Melbourne, FL (2003)
12. J. Han, J. Pei, Y. Yin, and R. Mao: Mining Frequent Patterns without Candidate Generation: A Frequent Pattern Tree Approach. Data Mining and Knowledge Discovery 8 (2004):53-87
13. R. J. Hilderman: Predicting Itemset Sales Profiles with Share Measures and Repeat-Buying Theory. In: J. Liu, Y. M. Cheung, H. Yin (eds.): Intelligent Data Engineering and Automated Learning. Lecture Notes in Computer Science, Vol. 2690. Springer-Verlag, Berlin Heidelberg New York (2003) 789-795
14. R. J. Hilderman, C. L. Carter, H. J. Hamilton, and N. Cercone: Mining Association Rules from Market Basket Data using Share Measures and Characterized Itemsets,"Intl. Journal of Artificial Intelligence Tools 7 (1998) 189-220
15. J. Liu, Y. Pan, K. Wang, and J. Han: Mining Frequent Item Sets by Opportunistic Projection. In: Proc. 8th ACM-SIGKDD Intl. Conf. on Knowledge Discovery and Dada Mining, Alberta, Canada (2002) 229-238
16. J. S. Park, M. S. Chen, and P. S. Yu: An Effective Hash-Based Algorithm for Mining Association Rules. In: Proc. 1995 ACM-SIGMOD Intl. Conf. on Management of Data, San Jose, CA (1995) 175-186
17. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang: H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In: Proc. 2001 IEEE Intl. Conf. on Data Mining, San Jose, CA (2001) 441-448
18. http://alme1.almaden.ibm.com/software/quest/Resources/datasets/syndata. html
19. http://www.cse.cuhk.edu.hk/~kdd/data/IBM_VC++.zip