A new concise representation of frequent itemsets using generators and a positive border

Knowledge and Information Systems

Volumn 17, Issue 1, 2008, Pages 35-56

  Liu, Guimei ^a   Li, Jinyan ^b   Wong, Limsoon ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Concise representation; Datamining; Frequent itemset mining; Generator; Positive border

Indexed keywords

DATA MINING;

CONCISE REPRESENTATIONS; DEPTH FIRST SEARCH STRATEGIES; FREQUENT CLOSED ITEMSETS; FREQUENT ITEMSET MINING; GENERATOR; MINIMUM SUPPORT THRESHOLDS; POSITIVE BORDER; STATE-OF-THE-ART ALGORITHMS;

REDUNDANCY;

EID: 54049129660     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-007-0111-5     Document Type: Article

References (30)

1. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proc of the 1993 ACM SIGMOD Conference, pp 207-216
2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proc of the 20th VLDB Conference, pp 487-499
3. Bastide Y, Pasquier N, Taouil R, Stumme G, Lakhal L (2000) Mining minimal non-redundant association rules using frequent closed itemsets. In: Proc of Computational Logic Conference, pp 972-986
4. Bastide Y, Taouil R, Pasquier N, Stumme G and Lakhal L (2000). Mining frequent patterns with counting inference. SIGKDD Explor 2(2): 66-75
5. Bonchi F and Lucchese C (2006). On condensed representations of constrained frequent patterns. Knowledge Inf Syst 9(2): 180-201
6. Borgelt C (2003) Efficient implementations of apriori and eclat. In: Proc of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations
7. Boulicaut J-F, Bykowski A and Rigotti C (2003). Free-sets: A condensed representation of boolean data for the approximation of frequency queries. Data Min Knowledge Discovery J 7(1): 5-22
8. Brijs T, Swinnen G, Vanhoof K, Wets G (2001) Using association rules for product assortment decisions: A case study. In: Proc of the 5th SIGKDD Conference, pp 254-260
9. Bykowski A, Rigotti C (2001) A condensed representation to find frequent patterns. In: Proc of the 20th PODS Symposium
10. Calders T, Goethals B (2002) Mining all non-derivable frequent itemsets. In: Proc of the 6th PKDD Conference, pp 74-85
11. Calders T, Goethals B (2003) Minimal k -free representations of frequent sets. In: Proc of the 7th PKDD Conference, pp 71-82
12. Calders T, Goethals B (2005) Depth-first non-derivable itemset mining. In: Proc of the 2005 SIAM International Data Mining Conference
13. Chi Y, Wang H, Yu PS and Muntz RR (2006). Catch the moment: Maintaining closed frequent itemsets over a data stream sliding window. Knowledge Inf Syst 10(3): 265-294
14. Grahne G, Zhu J (2003) Efficiently using prefix-trees in mining frequent itemsets. In: Proc of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations
15. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proc of the 2000 ACM SIGMOD Conference, pp 1-12
16. Han J, Wang J, Lu Y, Tzvetkov P (2002) Mining top-k frequent closed patterns without minimum support. In: Proc of the 2002 IEEE International Conference on Data Mining, pp 211-218
17. Bayardo RJ Jr (1998) Efficiently mining long patterns from databases. In: Proc of the 1998 ACM SIGMOD Conference, pp 85-93
18. Kryszkiewicz M (2001) Concise representation of frequent patterns based on disjunction-free generators. In: Proc of the 2001 ICDM Conference, pp 305-312
19. Kryszkiewicz M, Gajek M (2002) Concise representation of frequent patterns based on generalized disjunction-free generators. In: Proc of the 6th PAKDD Conference, pp 159-171
20. Liu G, Li J, Wong L, Hsu W (2006) Positive borders or negative borders: How to make lossless generator based representations concise. In: Proc of the 6th SIAM International Conference on Data Mining, pp 469-473
21. Mannila H, Toivonen H (1996) Multiple uses of frequent sets and condensed representations. In: Proc of the 2nd ACM SIGKDD Conference, pp 189-194
22. Pan F, Cong G, Tung AKH, Yang J, Zaki MJ (2003) Carpenter: Finding closed patterns in long biological datasets. In: Proc of the 9th ACM SIGKDD Conference, pp 637-642
23. Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proc of the 7th ICDT Conference, pp 398-416
24. Pei J, Dong G, Zou W, Han J (2002) On computing condensed frequent pattern bases. In: Proc of the 2002 ICDM Conference, pp 378-385
25. Pei J, Dong G, Zou W and Han J (2004). Mining condensed frequent-pattern bases. Knowledge Inf Syst 6(5): 570-594
26. Pei J, Han J, Mao R (2000) Closet: An efficient algorithm for mining frequent closed itemsets. In: Workshop on Research Issues in Data Mining and Knowledge Discovery, pp 21-30
27. Tzvetkov P, Yan X and Han J (2005). Tsp: Mining top-k closed sequential patterns. Knowledge Inf Syst 7(4): 438-457
28. Wang J, Pei J, Han J (2003) Closet+: Searching for the best strategies for mining frequent closed itemsets. In: Proc of the 9th ACM SIGKDD Conference, pp 236-245
29. Zaki MJ, Hsiao C-J (2002) Charm: An efficient algorithm for closed itemset mining. In: Proc of SIAM International Conference on Data Mining, pp 398-416
30. Zheng Z, Kohavi R, Mason L (2001) Real world performance of association rule algorithms. In: Proc of the 7th SIGKDD Conference, pp 401-406