A Mining Frequent Itemsets Algorithm in Stream Data Based on Sliding Time Decay Window

In order to reduce the time and memory consumption of frequent itemsets mining in stream data, and weaken the impact of historical transactions on data patterns, this paper proposes a frequent itemsets mining algorithm SWFIUT-stream based on sliding decay time window. In this algorithm, the time attenuation factor is introduced to assign different weights to each window unit to weaken their influence on data mode. In order to realize the fast stream data mining processing, when mining the frequent itemsets, the two-dimensional table is used to scan and decompose the itemsets synchronously to mine all the frequent itemsets in the window, and the distributed parallel computing processing is carried out based on storm framework. Experimental data show that the algorithm consumes less time and consumes less memory space than conventional algorithms when mining frequent itemsets in stream data.