Abstract—Discovery of association rule is one of the most
interesting areas of research in data mining, which extracts
together occurrence of itemset. In a dynamic database where
the new transaction are inserted into the database, keeping
patterns up-to-date and discovering new pattern are
challenging problems of great practical importance. This may
introduce new association rules and some existing association
rules would become invalid. It is important to study efficient
algorithms for incremental update of association rules in large
databases. In this paper, we modify an existing incremental
algorithm, Probability-based incremental association rule
discovery. The previous algorithm, probability-based
incremental association rule discovery algorithm uses principle
of Bernoulli trials to find frequent and expected frequent
k-itemsets. The set of frequent and expected frequent k-itemsets
are determined from a candidate k-itemsets. Generating and
testing the set of candidate is a time-consuming step in the
algorithm. To reduce the number of candidates 2-itemset that
need to repeatedly scan the database and check a large set of
candidate, our paper is utilizing a hash technique for the
generation of the candidate 2-itemset, especially for the
frequent and expected frequent 2-itemsets, to improve the
performance of probability-based algorithm. Thus, the
algorithm can reduce not only a number of times to scan an
original database but also the number of candidate itemsets to
generate frequent and expected frequent 2 itemsets. As a result,
the algorithm has execution time faster than the previous
methods. This paper also conducts simulation experiments to
show the performance of the proposed algorithm. The
simulation results show that the proposed algorithm has a good
performance.
Index Terms—Incremental Association Rule Discovery;
Association Rule Discovery; Data mining
Ratchadaporn Amornchewin is with the Faculty of Information
Technology, Thepsatri Rajabhat University, Lopburi, 15000, Thailand.
(e-mail: ramornchewin@yahoo.com).
Cite:Ratchadaporn Amornchewin, "Probability-based Incremental Association Rules Discovery Algorithm with Hashing Technique," International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 43-48, 2011.