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An effective algorithm for mining interesting quantitative association rules
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Source Symposium on Applied Computing archive
Proceedings of the 1997 ACM symposium on Applied computing table of contents
San Jose, California, United States
Pages: 88 - 90  
Year of Publication: 1997
ISBN:0-89791-850-9
Authors
Keith C. C. Chan  Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Wai-Ho Au  Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Sponsors
SIGCUE: ACM Special Interest Group on Computer Uses In Education
SIGADA: ACM Special Interest Group on Ada Programming Language
SIGAPP: ACM Special Interest Group on Applied Computing
SIGBIO: ACM Special Interest Group on Biomedical Computing
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 2,   Downloads (12 Months): 28,   Citation Count: 3
Additional Information:

references   cited by   index terms   collaborative colleagues  

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

1
Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
 
2
Keith C. C. Chan , Andrew K. C. Wong, APACS: a system for the automatic analysis and classification of conceptual patterns, Computational Intelligence, v.6 n.3, p.119-131, Aug. 1990  [doi>10.1111/j.1467-8640.1990.tb00129.x]
 
3
K. C. C. Chan, and A. K. C. Wong, "A Statistical Technique for Extracting Classificatory Knowledge from Databases", in G. Piatetsky-Shapiro, and W. J. Frawley (Eds.), Knowledge Discover3, in Databases, AAAI/MiT Press, 199 I, pp. 107-123.
 
4
John Y. Ching , Andrew K. C. Wong , Keith C. C. Chan, Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.7, p.641-651, July 1995  [doi>10.1109/34.391407]
 
5
Jiawei Han , Yongjian Fu, Discovery of Multiple-Level Association Rules from Large Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.420-431, September 11-15, 1995
 
6
Ramakrishnan Srikant , Rakesh Agrawal, Mining Generalized Association Rules, Proceedings of the 21th International Conference on Very Large Data Bases, p.407-419, September 11-15, 1995
7
Ramakrishnan Srikant , Rakesh Agrawal, Mining quantitative association rules in large relational tables, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.1-12, June 04-06, 1996, Montreal, Quebec, Canada

CITED BY  3
Keith C. C. Chan , Wai-Ho Au, Mining fuzzy association rules, Proceedings of the sixth international conference on Information and knowledge management, p.209-215, November 10-14, 1997, Las Vegas, Nevada, United States
Mathieu Serrurier , Didier Dubois , Henri Prade , Thomas Sudkamp, Learning fuzzy rules with their implication operators, Data & Knowledge Engineering, v.60 n.1, p.71-89, January, 2007
Thomas Sudkamp, Refinement of temporal constraints in fuzzy associations, International Journal of Approximate Reasoning, v.48 n.1, p.23-35, April, 2008

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining


General Terms:
Algorithms, Design, Experimentation, Performance, Theory


Keywords:
data mining, interestingness measure, negative association rules, positive association rules, quantitative association rules

Collaborative Colleagues:
Keith C. C. Chan: colleagues
Wai-Ho Au: colleagues

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