CFP-tree: A compact disk-based structure for storing and querying frequent itemsets

Information Systems

Volumn 32, Issue 2, 2007, Pages 295-319

  Liu, Guimei ^a   Lu, Hongjun ^a   Xu Yu, Jeffrey ^b  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Frequent itemset mining; Online interactive mining

Indexed keywords

ALGORITHMS; COMPACT DISKS; DATA STRUCTURES; DECISION SUPPORT SYSTEMS; INFORMATION RETRIEVAL; QUERY LANGUAGES;

CFP-TREE STRUCTURE; FREQUENT ITEMSET MINING; ONLINE INTERACTIVE MINING; ONLINE MINING REQUESTS;

DATA MINING;

EID: 33751196126     PISSN: 03064379     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.is.2005.11.004     Document Type: Article

References (44)

1. Agrawal R., Imielinski T., and Swami A.N. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD Conference (1993) 207-216
2. Pasquier N., Bastide Y., Taouil R., and Lakhal L. Discovering frequent closed itemsets for association rules. Proceedings of the 7th ICDT Conference (1999) 398-416
3. Bastide Y., Pasquier N., Taouil R., Stumme G., and Lakhal L. Mining minimal non-redundant association rules using frequent closed itemsets. Proceedings of Computational Logic Conference (2000) 972-986
4. Calders T., and Goethals B. Mining all non-derivable frequent itemsets. Proceedings of the 6th PKDD Conference (2002) 74-85
5. Dong G., and Li J. Efficient mining of emerging patterns: discovering trends and differences. Proceedings of the 5th SIGKDD Conference (1999) 43-52
6. Liu B., Hsu W., and Ma Y. Integrating classification and association rule mining. Proceedings of the 4th SIGKDD Conference (1998) 80-86
7. Nag B., Deshpande P.M., and DeWitt D.J. Using a knowledge cache for interactive discovery of association rules. Proceedings of the 5th ACM SIGKDD Conference (1999) 244-253
8. Morzy T., Wojciechowski M., and Zakrzewicz M. Materialized data mining views. PKDD (2000) 65-74
9. Cong G., and Liu B. Speed-up iterative frequent itemset mining with constraint changes. Proceedings of the 2002 ICDM Conference (2002) 107-114
10. Morzy T., and Zakrzewicz M. Group bitmap index: a structure for association rules retrieval. Proceedings of the 4th ACM SIGKDD Conference (1998) 284-288
11. Aggarwal C.C., and Yu P.S. Online generation of association rules. Proceedings of the 14th IEEE ICDE Conference (1998) 402-411
12. Tuzhilin A., and Liu B. Querying multiple sets of discovered rules. Proceedings of the 8th ACM SIGKDD Conference (2002) 52-60
13. Liu G., Lu H., Lou W., and Yu J.X. On computing, storing and querying frequent patterns. Proceedings of the 9th ACM SIGKDD Conference (2003) 607-612
14. Srikant R., Vu Q., and Agrawal R. Mining association rules with item constraints. Proceedings of the 3rd ACM SIGKDD Conference (1997) 67-73
15. Ng R.T., Lakshmanan L.V.S., Han J., and Pang A. Exploratory mining and pruning optimizations of constrained association rules. Proceedings of the 1998 ACM SIGMOD Conference (1998) 13-24
16. Lakshmanan L.V.S., Leung C.K.-S., and Ng R.T. Efficient dynamic mining of constrained frequent sets. ACM Trans. Database Syst. (TODS) 28 4 (2003)
17. Lakshmanan L.V.S., Ng R.T., Han J., and Pang A. Optimization of constrained frequent set queries with 2-variable constraints. Proceedings of the 1999 ACM SIGMOD Conference (1999) 157-168
18. Pei J., and Han J. Can we push more constraints into frequent pattern mining?. Proceedings of the 6th ACM SIGKDD Conference (2000) 350-354
19. Pei J., Han J., and Lakshmanan L.V.S. Mining frequent item sets with convertible constraints. Proceedings of the 17th ICDE Conference (2001)
20. Grahne G., Lakshmanan L.V.S., and Wang X. Efficient mining of constrained correlated sets. Proceedings of the 16th ICDE Conference (2000) 512-521
21. Bucila C., Gehrke J., Kifer D., and White W.M. Dualminer: a dual-pruning algorithm for itemsets with constraints. Proceedings of the 8th ACM SIGKDD Conference (2002) 42-51
22. Bykowski A., and Rigotti C. A condensed representation to find frequent patterns. Proceedings of the 20th PODS Symposium (2001)
23. Kryszkiewicz M., and Gajek M. Concise representation of frequent patterns based on generalized disjunction-free generators. Proceedings of the 6th PAKDD Conference (2002) 159-171
24. Calders T., and Goethals B. Minimal k-free representations of frequent sets. Proceedings of the 7th PKDD Conference (2003) 71-82
25. Pan F., Cong G., Tung A.K.H., Yang J., and Zaki M.J. Carpenter: finding closed patterns in long biological datasets. Proceedings of the 9th ACM SIGKDD Conference (2003) 637-642
26. J. Pei, J. Han, R. Mao, Closet: an efficient algorithm for mining frequent closed itemsets, in: Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000, pp. 21-30.
27. Wang J., Pei J., and Han J. Closet +: searching for the best strategies for mining frequent closed itemsets. Proceedings of the 9th ACM SIGKDD Conference (2003) 236-245
28. Zaki M.J., and Hsiao C.-J. Charm: an efficient algorithm for closed itemset mining. Proceedings of SIAM International Conference on Data Mining (2002) 398-416
29. Calders T., and Goethals B. Depth-first non-derivable itemset mining. Proceedings of the 2005 SIAM International Data Mining Conference (2005)
30. Pei J., Dong G., Zou W., and Han J. On computing condensed frequent pattern bases. Proceedings of the 2002 ICDM Conference (2002) 378-385
31. Boulicaut J.-F., Bykowski A., and Rigotti C. Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Min. Knowl. Discovery J. 7 1 (2003) 5-22
32. Ayan N.F., Tansel A.U., and Arkun M.E. An efficient algorithm to update large itemsets with early pruning. Proceedings of the 5th SIGKDD Conference (1999) 287-291
33. Cheung D.W., Han J., Ng V.T., and Wong C. Maintenance of discovered association rules in large databases: an incremental updating technique. Proceedings of the 12th IEEE ICDE Conference (1996) 106-114
34. R. Feldman, Y. Aumann, A. Amir, H. Mannila, Efficient algorithms for discovering frequent sets in incremental databases, in: Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997.
35. Thomas S., Bodagala S., Alsabti K., and Ranka S. An efficient algorithm for the incremental updation of association rules in large databases. Proceedings of the 3rd ACM SIGKDD Conference (1997) 263-266
36. Raymon R. Search through systematic set enumeration. Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (1992)
37. Han J., Pei J., and Yin Y. Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD Conference (2000) 1-12
38. Liu G., Lu H., Lou W., Xu Y., and Yu J.X. Efficient mining of frequent patterns using ascending frequency ordered prefix-tree. Data Min. Knowl. Discovery J. 9 3 (2004) 249-274
39. Agarwal R.C., Aggarwal C.C., and Prasad V.V.V. Depth first generation of long patterns. Proceedings of the 6th ACM SIGKDD Conference (2000) 108-118
40. Zheng Z., Kohavi R., and Mason L. Real world performance of association rule algorithms. Proceedings of the 7th SIGKDD Conference (2001) 401-406
41. Brijs T., Swinnen G., Vanhoof K., and Wets G. Using association rules for product assortment decisions: a case study. Proceedings of the 5th SIGKDD Conference (2001) 254-260
42. Grahne G., and Zhu J. Efficiently using prefix-trees in mining frequent itemsets. Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations (2003)
43. Goethals B., and Zaki M.J. Advances in frequent itemset mining implementations: introduction to fimi03. Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations (2003)
44. Helmer S., and Moerkotte G. A performance study of four index structures for set-valued attributes of low cardinality. VLDB J. 12 3 (2003) 244-261