Inferring knowledge from frequent patterns

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2311, Issue , 2002, Pages 247-262

  Kryszkiewicz, Marzena ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

EPISODE RULES; ITERATIVE TRANSFORMATION; LOSSLESS; SEQUENTIAL PATTERNS;

ITERATIVE METHODS;

EID: 84958764467     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46019-5_19     Document Type: Conference Paper

References (7)

1. Agrawal, R., Imielinski, T., Swami, A.: Mining Associations Rules between Sets of Items in Large Databases. In: Proc. of the ACM SIGMOD Conference on Management of Data. Washington, D.C. (1993) 207-216
2. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Advances in Knowledge Discovery and Data Mining. AAAI, Menlo Park, California (1996) 307-328
3. Kryszkiewicz, M.: Representative Association Rules. In: Proc. of PAKDD’98. Melbourne, Australia. LNAI 1394. Springer-Verlag (1998) 198-209
4. Kryszkiewicz, M.: Mining with Cover and Extension Operators. In: Proc. of PKDD’00. Lyon, France. LNAI 1910. Springer-Verlag (2000) 476-482
5. Kryszkiewicz, M.: Inducing Theory for the Rule Set. In: Proc. of RSCTC’00. Banff, Canada (2000) 353-360
6. Kryszkiewicz, M.: Concise Representation of Frequent Patterns based on Disjunction-free Generators. In: Proc. of ICDM’2001, IEEE Computer Society Press (2001).
7. Pasquier N., Bastide Y., Taouil R., Lakhal L., Efficient Mining of Association Rules Using Closed Itemset Lattices. Information Systems 24 (1999) 25-46