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An Effective Boolean Algorithm for Mining Association Rules in Large Databases
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Suh-Ying Wur and
Yungho Leu
View Paper (PDF)
Return to Session 4A: Data Analysis and Mining
In this paper, we present
an effective Boolean algorithm for mining association rules in large databases
of sales transactions. Like the Apriori algorithm, the proposed Boolean algorithm
mines association rules in two steps. In the first step, logic OR and AND
operations are used to compute frequent itemsets. In the second step, logic AND
and XOR operations are applied to derive all interesting association rules based
on the computed frequent itemsets. By only scanning the database once and
avoiding generating candidate itemsets in computing frequent itemsets, the
Boolean algorithm gains a significant performance improvement over the Apriori algorithm.
We propose two efficient implementations of the Boolean algorithm, the BitStream
approach and the Sparse-Matrix approach. Through comprehensive experiments, we
show that both the BitStream approach and the Sparse-Martrix approach
outperform the Apriori algorithm in all database settings. Especially, the
Sparse-Matrix approach shows a very significant performance improvement over
the Apriori algorithm.
Copyright(C) 2000 ACM
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