ACM Home Page
Please provide us with feedback. Feedback
Digital Library logoTake a look at the new version of this page: [ beta version ]. Tell us what you think.
Mining association rules between sets of items in large databases
Full text PdfPdf (1.08 MB)
Source International Conference on Management of Data archive
Proceedings of the 1993 ACM SIGMOD international conference on Management of data table of contents
Washington, D.C., United States
Pages: 207 - 216  
Year of Publication: 1993
ISBN:0-89791-592-5
Also published in ...
Authors
Rakesh Agrawal  IBM Almaden Research Center, 650 Harry Road, San Jose, CA
Tomasz Imieliński  Computer Science Department, Rutgers University, New Brunswick, NJ
Arun Swami  IBM Almaden Research Center, 650 Harry Road, San Jose, CA
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 208,   Downloads (12 Months): 1553,   Citation Count: 1134
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/170035.170072
What is a DOI?

ABSTRACT

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.


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
 
2
 
3
Dina Bitton, "Bridging the Gap Between Database Theory and Practice", Cadre Technologies, Menlo Park, 1992.
 
4
L. Breiman, j. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, 1984.
 
5
 
6
M. Kokar, "Discovering Functional Formulas through Changing Representation Base", Proceedings of the Fifth National Conference on Artificial Intelligence, 1986, 455-459.
 
7
 
8
 
9
 
10
G. Piatetsky-Shapiro, Discovery, Analysis, and Presentation of Strong Rules, In {11}, 229-248.
 
11
12
 
13
L.G. Valiant, "Learning Disjunctions and Conjunctions", IJCAI-85, Los Angeles, 1985, 560-565.
 
14
Yi-Hua Wu and Shulin Wang, Discovering Functional Relationships from Observational Data, In {11}, 55-70.

CITED BY  1,134