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Mining frequent itemsets is one of the most
important concepts in data mining. It is a fundamental and
initial task of data mining. Apriori[3] is the most popular and
frequently used algorithm for finding frequent itemsets.
There are other algorithms viz, Eclat[4], FPgrowth[5] which
are used to find out frequent itemsets. In order to improve
the time efficiency of Apriori algorithms, Jiemin Zheng
introduced BitApriori[1] algorithm with the following
corrections with respect to Apriori[3] algorithm.
1) Support count is implemented by performing bitwise “And”
operation on binary strings
2) Special equalsupport pruning
In this paper, to improve the time efficiency of BitApriori[1]
algorithm, a novel algorithm that deletes infrequent items
during trie2 and subsequent tire’s are proposed and
demonstrated with an example.
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