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Pruning techniques in associative classification: Survey and comparison

Thabtah, Fadi Abdeljaber (2006) Pruning techniques in associative classification: Survey and comparison. Journal of Digital Information Management, 4 (3). pp. 197-202. ISSN 0972-7272

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Association rule discovery and classification
are common data mining tasks. Integrating association
rule and classification also known as associative
classification is a promising approach that derives
classifiers highly competitive with regards to accuracy to
that of traditional classification approaches such as rule
induction and decision trees. However, the size of the
classifiers generated by associative classification is often
large and therefore pruning becomes an essential task.
In this paper, we survey different rule pruning methods
used by current associative classification techniques.
Further, we compare the effect of three pruning methods
(database coverage, pessimistic error estimation, lazy
pruning) on the accuracy rate and the number of rules
derived from different classification data sets. Results
obtained from experimenting on different data sets from
UCI data collection indicate that lazy pruning algorithms
may produce slightly higher predictive classifiers than
those which utilise database coverage and pessimistic
error pruning methods. However, the potential use of such
classifiers is limited because they are difficult to
understand and maintain by the end-user.

Item Type: Article
Additional Information: © Reproduced by permission of Journal of Digital Information Management Published by Digital Information Research Foundation
Uncontrolled Keywords: Associative Classification, Association Rule, Classification, Data Mining, Rule Pruning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering

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Depositing User: Sara Taylor
Date Deposited: 05 Jul 2007
Last Modified: 15 Jan 2017 17:26


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