Tough constraint-based frequent closed itemsets mining

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2003, Pages 416-420

  Jia, Lei ^a   Pei, Renqing ^a   Pei, Dingyu ^b  

^a SHANGHAI UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

Author keywords

Association rule; Frequent closed itemsets; Tough Constraints

Indexed keywords

ALGORITHMS; COMPUTER SCIENCE; CONSTRAINT THEORY; DATABASE SYSTEMS;

FREQUENT CLOSED ITEMSETS;

DATA MINING;

EID: 0037998998     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/952532.952615     Document Type: Conference Paper

References (14)

1. R.Agrawal,T.Imielinski and A.Swami. Mining association rules between sets of items in large databases. SIGMOD 93,pp 207-216.
2. R.Agrawal and R.Srikant. Fast slgorithms for mining association rules. VLDB 94,pp 487-499.
3. J.Han and Y.Fu. Discovery of multiple-level association rules from large databases. VLDB 95,pp 420-431.
4. R.Srikant,Q.Vu,and R.Agrawal. Mining associstion rules with item constraints. In Proc. 1997 Int. Conf. Knowledge Discovery and Data mining (KDD'97), pages67-73, NewBeach, CA, Aug. 1997.
5. R.Ng,L.V.S.Lakshmanan,J.Han,and A.Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98),pages 13-24,Seattle,WA,June 1998.
6. J.Pei,J.Han,and L.V.S.Lakshmanan. Mining frequent itemsets with convertible constraints. In ICDE'01, pages 323-332.
7. J.Han,J,Pei and Y.Yin. Mining frequent patterns without candidate generation. In SIGMOD'00, pages 1-12.
8. Bayardo,R.J. Efficiently mining long patterns from databases. In Proc. of the 1998 ACM SIGKDD Int'l Conf. on Management of Data, 85-93.
9. L.V.Lakshmanan,R.Ng,J.Han, and A.Pang. Optimization of constrained frequent set queries with2-variable constraints. In proceedings of ACM SIGMOD Conference on Management of Data (SIGMOD'99), pages 157-168, Philadelphia, USA, 1999, ACM Press.
10. J.-F Boulicaut and B.Jeudy. Using constraint for itemset mining: should we prune or not? In proceedings Bases de Donnees Advances "BDA '00,pages 221-237,Blois, F, Oct. 2000.
11. N.Pasquier,Y.Bastide,R.Taouil, and L.Lakhal. Efficient mining of association rules using closed itemset lattices. Information Systems, 24(1): 25-46,Jan.1999.
12. J.-F.Boulicaut and A.Bykowski. Frequent closures as a concise representation for binary data mining. In Proceedings of the Fourth Pacif-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'00), volume 1805 of Lecture Notes in Artificial Intelligence, pages 62-73,Kyoto, JP, Apr.2000.Springer-Verlag.
13. M.J.Zaki. Generating non-redundant association rules. In Proc.KDD'00,pages34-43, Boston, USA, 2000
14. J.Pei,J.Han,and R.Mao. CLOSET: an efficient algorithm for mining frequent closed itemsets. In proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD'00), Dallas, USA, May 2000.