A frequent itemset mining algorithm based on the Principle of Inclusion-Exclusion and transaction mapping

This paper proposes a novel frequent itemsets mining algorithm called PIETM (Principle of Inclusion-Exclusion and Transaction Mapping). PIETM has three major features. First, similar to the mining process in Apriori, PIETM discovers frequent itemsets in a bottom-up manner. However, it reduces database scanning to only two times. Second, PIETM does not scan the database to count the itemsets' support. Instead, it employs the Principle of Inclusion-Exclusion to calculate the support of candidate itemsets. Third, PIETM uses transaction intervals to map and store the transaction ids of each item, which facilitates the itemsets counting process. We also present experimental results comparing PIETM with existing algorithms in this paper. The results show that PIETM takes lower execution time than other methods when the dataset has numerous items. In summary, this paper makes three major contributions. First, it presents a new method that calculates the support of itemsets using a well-known set theory property. Second, it demonstrates the correctness of counting itemsets in PIETM. Third, our method demonstrates its suitability for different high-performance applications by combining the advantages of the simplicity of Apriori and efficiency of FP-growth.