Mining association rules with item constraints
1997
767 citations
Abstract
The problem of discovering association rules has re-ceived considerable research attention and several fast algorithms for mining association rules have been de-veloped. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a specific item or rules that contain children of a specific item in a hierar-chy. While such constraints can be applied as a post-processing step, integrating them into the mining algo-rithm can dramatically reduce the execution time. We consider the problem of integrating constraints that n..,, l.....l,....,....,,....:,,,-1.~.. cl.,-..s..a..-m e....l.“,“, CUG Y”“Ac;Qu GnpLz:I)DIVua “YGI “Us: pGYaLcG “I OLJDciliLG of items into the association discovery algorithm. We present three integrated algorithms for mining asso-ciation rules with item constraints and discuss their tradeoffs. 1.
Keywords
Association rule learningComputer scienceData miningAssociation (psychology)HierarchyMachine learning
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Publication Info
- Year
- 1997
- Type
- article
- Pages
- 67-73
- Citations
- 767
- Access
- Closed
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Cite This
Ramakrishnan Srikant,
Quoc Vu,
Rakesh Agrawal
(1997).
Mining association rules with item constraints.
, 67-73.