Quantitative association rules based on half-spaces: An optimization approach

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 507-510

  Rückert, Ulrich ^a   Richter, Lothar ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; ITERATIVE METHODS; OPTIMIZATION; SET THEORY; SIMULATED ANNEALING; VECTORS;

ASSOCIATION RULE MINING; DISCRETIZATION; QUANTITATIVE ASSOCIATION RULES; SEPARATION FUNCTIONS;

KNOWLEDGE BASED SYSTEMS;

EID: 19544381052     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Y. Aumann and Y. Lindell. A statistical theory for quantitative association rules. Journal of Intelligent Information Systems, 20(3):255-283, 2003.
2. C. Blake and C. Merz. UCI repository of machine learning databases, 1998.
3. T. Hughes et al. Functional discovery via a compendium of expression profiles. Cell, 102:109-126, July 2000.
4. U. Rückert, L. Richter, and S. Krämer. Quantitative association rules based on half-spaces: An optimization approach. Technical Report TUM-I0412, Institut für Informatik, TU München, 2004.
5. R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. In H. V. Jagadish and I. S. Mumick, editors, Proc. of the 1996 ACM SIGMOD International Conference on Management of Data, pages 1-12, 1996.
6. G. I. Webb. Discovering associations with numeric variables. In Proc. of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 383-388. ACM Press, 2001.
7. J. Wijsen and R. Meersman. On the complexity of mining quantitative association rules. Data Mining and Knowledge Discovery, 2(3):263-281, 1998.