A Novel Approach for Finding Frequent Itemsets in Data Stream

Abstract

The paper proposes a novel methodology of finding frequent itemsets in data stream. Fuzzification of support of the closed frequent itemsets in conjunction with a jumping window has been used for finding frequent itemsets. Closed frequent itemsets help in retaining all frequent itemsets in a reduced memory space. Fuzzifying the support of the closed frequent itemsets helps in preserving information regarding frequent itemsets at different time intervals in the data stream. The use of the jumping window over the data stream improves the speed of the proposed algorithm. Effectiveness of the proposed algorithm in terms of execution time and accuracy is shown by comparing its performance with the widely known MOMENT algorithm on both IBM synthetic data sets and benchmark data sets taken from the UCI Machine Learning Repository.

An Algorithm for Mining Frequent Itemsets from Library Big Data

“…While many algorithms [7][8][9][10][11][12][13] have significantly increased their performance, they are still not fast enough for dealing with large datasets. This situation gave rise to the development of parallel algorithms.…”