Discovery of interesting association rules using genetic algorithm with adaptive mutation
Kabir, MMJ and Xu, S and Kang, BH and Zhao, Z, Discovery of interesting association rules using genetic algorithm with adaptive mutation, Lecture Notes in Computer Science: 22nd International Conference, ICONIP 2015 - Neural Information Processing, 09-12 November 2015, Istanbul, Turkey, pp. 96-105. ISBN 978-3-319-26534-6 (2015) [Refereed Conference Paper]
Association rule mining is the process of discovering useful and
interesting rules from large datasets. Traditional association rule mining algorithms
depend on a user specified minimum support and confidence values.
These constraints introduce two major challenges in real world applications:
exponential search space and a dataset dependent minimum support value. Data
analyzers must specify suitable dataset dependent minimum support value for
mining tasks although they might have no knowledge regarding the dataset and
these algorithms generate a huge number of unnecessary rules. To overcome
these kinds of problems, recently several researchers framed association rule
mining problem as a multi objective problem. In this paper, we propose
ARMGAAM, a new evolutionary algorithm, which generates a reduced set of
association rules and optimizes several measures that are present in different
degrees based on the datasets are used. To accomplish this, our method extends
the existing ARMGA model for performing an evolutionary learning, while
introducing a reinitialization process along with an adaptive mutation method.
Moreover, this approach maximizes conditional probability, lift, net confidence
and performance in order to obtain a set of rules which are interesting, useful
and easy to comprehend. The effectiveness of the proposed method is validated
over a few real world datasets.
Refereed Conference Paper
data mining, ARMGA, positive association rules, genetic algorithm, conditional probability