Quotation Hahsler, Michael, Buchta, Christian, Hornik, Kurt. 2008. Selective Association Rule Generation.. Computational Statistics 23 (2): 303-315.




Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confidence or to filter the found rules using increasingly strict constraints on additional measures of interestingness until the set of rules found is reduced to a manageable size. In this paper we describe a different approach which is based on the idea to first define a set of “interesting” itemsets (e.g., by a mixture of mining and expert knowledge) and then, in a second step to selectively generate rules for only these itemsets. The main advantage of this approach over increasing thresholds or filtering rules is that the number of rules found is significantly reduced while at the same time it is not necessary to increase the support and confidence thresholds which might lead to missing important information in the database.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Computational Statistics
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title Selective Association Rule Generation.
Volume 23
Number 2
Year 2008
Page from 303
Page to 315
Reviewed? Y


Hahsler, Michael (Former researcher)
Buchta, Christian (Former researcher)
Hornik, Kurt (Details)
Institute for Marketing and Customer Analytics IN (Details)
Institute for Data, Process and Knowledge Management (AE Sabou) (Details)
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
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