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