Research And Application Of Closed Frequent Itemset Mining Algorithm In ABC Inventory Management Optimization Problem

The reasonable classification of inventory materials is very important to the inventory management activities of enterprises.For each kind of materials,it can reduce the inventory cost and optimize the inventory management activities by formulating the corresponding inventory control strategy according to its nature.Among them,ABC inventory classification method is the most basic method.Most of the existing ABC classification studies only consider the independent demand for materials,without considering the internal correlation between materials.Frequent itemsets mining algorithm of frequent patterns contain the inner link between the material and can be used to optimize the ABC inventory classification,but a huge number of mining the frequent patterns can lead to some repeated,contradictions in the adjustment may occur when the inventory classification problem,and the use of the characteristics of frequent closed itemsets compression condition can solve this problem well.Closed frequent itemsets are a simplified representation of related frequent itemsets.Mining closed frequent itemsets greatly reduces the number of frequent itemsets in mining results,which has become an important research topic in data mining in recent years.Several closed frequent item set mining algorithms have been proposed,which can be applied to the ABC inventory classification optimization problem.DCI?Closed algorithm is a classic closed frequent item set mining algorithm.Through analysis,it is found that there is still room for improvement in pruning strategy and the efficiency of the algorithm needs to be improved.Therefore,this paper proposes a new pruning strategy to optimize the search space of DCI?Closed algorithm,and then proposes an improved algorithm DCI?ESCS.Then,the DCI?ESCS algorithm is used to optimize the ABC inventory classification problem.The research contents are as follows:(1)In this paper,the Estimated Support co-occurrence Structure(ESCS),which stores all the Support information of 2-item sets,is applied to the classical closed frequent item set mining algorithm DCI?Closed,and an ESCS pruning strategy for 2-item sets is proposed.Finally,the improved DCI?ESCS algorithm is obtained.Experiments were carried out on five data sets of CONNECT,PUMSB,Chess,PUMSB?STAR and Accidents in SPMF open resource database under different minimum support thresholds to compare and analyze the time performance of the algorithm before and after improvement.Experimental results show that the improved DCI?ESCS algorithm performs well on long and dense data sets,and its time efficiency is improved to some extent.(2)In this paper,DCI?ESCS algorithm is applied to the optimization problem of material-related ABC inventory management.First,the original data are preliminally classified as the basis for classification adjustment in the later stage.Then,the closed frequent patterns are mined and the effective patterns are screened by the improved DCI?ESCS algorithm.Finally,some initial classifications are adjusted by the correlation of the materials in the effective patterns.Through further analysis,it can be concluded that the adjusted inventory classification can optimize inventory management,reduce inventory cost,and improve service level.