PADS: A simple yet effective pattern-aware dynamic search method for fast maximal frequent pattern mining

Knowledge and Information Systems

Volumn 20, Issue 3, 2009, Pages 375-391

  Zeng, Xinghuo ^a   Pei, Jian ^a   Wang, Ke ^a   Li, Jinyan ^b  

^a SIMON FRASER UNIVERSITY   (Canada)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Frequent pattern mining; Max pattern mining; Pattern aware search

Indexed keywords

COMPUTER OPERATING SYSTEMS;

EMPIRICAL STUDIES; ENUMERATION TREES; FREQUENT PATTERN MINING; MAXIMAL FREQUENT PATTERN; MAXIMAL FREQUENT PATTERN MININGS; PATTERN MINING; PATTERN MINING ALGORITHMS; PATTERN-AWARE SEARCH;

DATA MINING;

EID: 69949168883     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-008-0179-6     Document Type: Article

References (32)

1. Agarwal RC, Aggarwal CC, Prasad VVV (2000) Depth first generation of long patterns. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM Press, Boston, Massachusetts, United States, pp 108-118
2. Aggarwal CC (2001) Towards long pattern generation in dense databases. SIGKDD Explor Newsl 3(1):20-26
3. Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of 1998 ACM-SIGMOD international conference management of data (SIGMOD'98). Seattle WA, pp 94-105
4. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of 1993 ACM-SIGMOD international conference management of data (SIGMOD'93). Washington, DC, pp 207-216
5. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 1994 internatinal conference on very large data bases (VLDB'94). Santiago, Chile, pp 487-499
6. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of 1995 international conference on data engineering (ICDE'95). Taipei, Taiwan, pp 3-14
7. Bayardo RJ (1998) Efficiently mining long patterns from databases. In: Proceedings of 1998 ACM-SIGMOD international conference management of data (SIGMOD'98). Seattle, WA, pp 85-93
8. Beyer K, Ramakrishnan R (1999) Bottom-up computation of sparse and iceberg cubes. In: Proceedings of 1999 ACM-SIGMOD international conference on management of data (SIGMOD'99). Philadelphia, PA, pp 359-370
9. Bonchi F, Lucchese C (2006) On condensed representations of constrained frequent patterns. Knowl Inf Syst 9(2):180-201
10. Brin S, Motwani R, Silverstein C (1997) Beyond market basket: Generalizing association rules to correlations. In: Proceedings of 1997 ACM-SIGMOD international conference on management of data (SIGMOD'97). Tucson, Arizona, pp 265-276
11. Burdick D, Calimlim M, Gehrke J (2001) MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Proceedings of 2001 international conference on data engineering (ICDE'01). Heidelberg, Germany, pp 443-452
12. Dong G, Li J (1999) Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of 1999 international conference on knowledge discovery and data mining (KDD'99). San Diego, CA, pp 43-52
13. Dong G, Li J (2005) Mining border descriptions of emerging patterns from dataset pairs. Knowl Inf Syst 8(2):178-202
14. Gouda K, Zaki MJ (2001) Efficiently mining maximal frequent itemsets. In: ICDM, pp 163-170 http://citeseer.nj.nec.com/499930.html
15. Grahne G, Zhu J (2005) Fast algorithms for frequent itemset mining using fp-trees. IEEE Trans Knowl Data Eng 17(10):1347-1362
16. Han J, Dong G, Yin Y (1999) Efficient mining of partial periodic patterns in time series database. In: Proceedings of 1999 international conference data on engineering ICDE'99). Sydney, Australia, pp 106-115
17. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of 2000 ACM-SIGMOD international conference on management of data (SIGMOD'00). Dallas, TX, pp 1-12
18. Li J, Dong G, Ramamohanarao K, Wong L (2004) Deeps: A new instance-based lazy discovery and classification system. Mach. Learn. 54(2):99-124
19. Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceedings of 1998 international conference on knowledge discovery and data mining (KDD'98). New York, NY, pp 80-86
20. Liu G, Li J, Wong L (n.d.) A new concise representation of frequent itemsets using generators and a positive border. Knowl Inf Syst (accepted)
21. Luo C, Chung SM (n.d.) A scalable algorithm for mining maximal frequent sequences using a sample. Knowl Inf Syst (accepted)
22. Mannila H, Toivonen H, Verkamo AI (1997) Discovery of frequent episodes in event sequences. Data Mining Knowl Discov 1:259-289
23. Ordonez C, Ezquerra N, Santana CA (2006) Constraining and summarizing association rules in medical data. Knowl Inf Syst 9(3):1-2
24. Pei J, Dong G, Zou W, Han J (2004) Mining condensed frequent-pattern bases. Knowl Inf Syst 6(5):570-594
25. Pei J, Han J, Lu H, Nishio S, Tang S, Yang D (2001) H-Mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings of 2001 international conference on data mining (ICDM'01). San Jose, CA, pp 441-448
26. Pei J, Han J, Mao R (2000) CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Proceedings of 2000 ACM-SIGMOD international workshop on data mining and knowledge discovery (DMKD'00)', Dallas, TX, pp 11-20
27. Rymon R (1992) Search through systematic set enumeration. In: Proceedings of 1992 international conference on principle of knowledge representation and reasoning (KR'92). Cambridge, MA, pp 539-550
28. Sayal M, Scheuermann P (2001) Distributed web log mining using maximal large item sets'. Knowl Inf Syst 3(4):389-404
29. Silverstein C, Brin S, Motwani R, Ullman J (1998) Scalable techniques for mining causal structures. In: Proceedings of 1998 international conference on very large data bases (VLDB'98). New York, NY, pp 594-605
30. Uno T, Kiyomi M, Arimura H (2004) Lcm ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets. In: Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations (FIMI'04). Brighton, UK
31. Yang G (2004) The complexity of mining maximal frequent itemsets and maximal frequent patterns. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (KDD'04). ACM Press
32. Zaki MJ, Hsiao CJ (1999) Charm: An efficient algorithm for closed association rule mining. In: Technical report 99-10, Computer Science. Rensselaer Polytechnic Institute