Abstract:  In the existing researches on closed frequent item set mining algorithms, pruning strategies are relatively single, most of which are for 1item set pruning, and there are relatively few pruning strategies for 2item set and nitem set (n逸3). However, effective pruning strategies can find and cut off a large number of hopeless item sets in advance. Therefore, improving the pruning strategy of closed frequent item set is of great help to improve the efficiency of this kind of algorithm. On the basis of ESCS(Estimated Support Cooccurrence Structure) structure, an ESCS pruning strategy for 2itemsets is proposed, and the classical closed frequent itemset mining algorithm DCI_Closed(Direct Count Intersect Closed) is improved to DCI_ESCS(Direct Count Intersect Estimated Support Cooccurrence Structure) algorithm, and the effect of ESCS pruning strategy is verified. On multiple public datasets and under different minimum support thresholds, experiments are conducted to compare the time performance of the algorithm before and after the improvement. The experimental results show that the improved DCI_ESCS algorithm performs well on long and dense data sets with long transaction and itemsets, and the time efficiency is improved to a certain extent.

Key words: closed frequent itemsets')">

closed frequent itemsets, pruning strategy, data mining