Concise representation offrequent patterns based on generalized disjunction-free generators

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

Volumn 2336, Issue , 2002, Pages 159-171

  Kryszkiewicz, Marzena ^a   Gajek, Marcin ^a  

^a WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

CONCISE REPRESENTATIONS; DATA MINING PROBLEMS; EPISODE RULES; ITEM SETS; LOSSLESS; SEQUENTIAL PATTERNS;

DATA MINING;

EID: 84945313149     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47887-6_15     Document Type: Conference Paper

References (9)

1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Advances in KDD. AAAI, Menlo Park, California (1996) 307-328
2. Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining Frequent Patterns with Counting Inference. ACM SIGKDD Explorations, Vol. 2(2). (2000) 66-75
3. Bykowski, A., Rigotti, C.: A Condensed Representation to Find Frequent Patterns. In: Proc. of the 12thACM SIGACT-SIGMOD-SIGART PODS’ 01 (2001)
4. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2000)
5. Kryszkiewicz, M.: Closed Set based Discovery of Representative Association Rules. In: Proc. of IDA '01. Springer (2001) 350-359
6. Kryszkiewicz, M.: Concise Representation of Frequent Patterns based on Disjunction-Free Generators. In: Proc. of ICDM '01. IEEE (2001) 305-312
7. Mannila, H., Toivonen, H.: Levelwise Search and Borders of Theories in Knowledge Discovery. In: Data Mining and Knowledge Discovery 1(3). (1997) 241-258
8. Pasquier, N.: DM: Algorithmes d'extraction et de reduction des regies d'association dans les bases de donnees. Ph.D. Thesis. Univ. Pascal-Clermont-Ferrand II (2000)
9. Zaki, M.J.: Generating Non-redundantAssociation Rules. In: 6th ACM SIGKDD (2000)