Affiliations: Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran
Correspondence:
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Corresponding author: Azadeh Soltani, Department of Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran. E-mail:[email protected]
Abstract: Confabulation-based Association Rule Mining (CARM) is an
algorithm that is inspired by the thought process of human brain. It is an
iterative method where, in each iteration, new rules are generated using a
measure of cogency and constraints on the antecedents and consequents of the
previous iteration. This cogency measure leads to a particular ability in
dealing with rare items problem. Here, we aim to study CARM's antecedent
constraint with respect to the ability of pruning uninteresting rules.
Hence, a new Tree-based CARM (TCARM) algorithm is proposed which produces
all rules using only cogency constraint on the consequents. In our
experiments, the extracted rules of these two algorithms are compared with
those of CFPGrowth using several measures such as precision, recall, support
and cogency of rules. CFPGrowth is an approach that finds association rules
using multiple minimum support and confidence, so the extracted rules of
CFPGrowth support rare items and can be used for evaluating the extracted
rules of CARM and TCARM. Our analysis based on the proposed TCARM and the
extracted rules of CFPGrowth, on both synthetic and real datasets, show that
CARM prunes uninteresting rules better than TCARM, which confirms that its
antecedent constraint can help to control the number of extracted rules
properly.
Keywords: Association rule mining, CARM, cogency, confabulation theory, rare items
