A Further Study on Inverse Frequent Set Mining

Abstract

Frequent itemset mining is a common task in data mining from which association rules are derived. As the frequent itemsets can be considered as a kind of summary of the original databases, recently the inverse frequent set mining problem has received more attention because of its potential threat to the privacy of the original dataset. Since this inverse problem has been proven to be NP-complete, people ask “Are there reasonably efficient search strategies to find a compatible data set in practice?” [1]. This paper describes our effort towards finding a feasible solution to address this problem.