An Efficient Approach for Mining Frequent Itemsets with Large Windows
The problem of mining frequent itemsets in streaming data has attracted a lot of attention lately. Even though numerous frequent itemsets mining algorithms have been developed over the past decade, new solutions for handling stream data are still required due to the continuous, unbounded, and ordered sequence of data elements generated at a rapid rate in a data stream. The main challenge in data streams will be constrained by limited resources of time, memory, and sample size. Data mining has traditionally been performed over static datasets, where data mining algorithms can afford to read the input data several times. The goal of this paper is analyzing the mining frequent itemsets in theoretical manner in the large windows.