Mining erasable itemsets with subset and superset itemset constraints

Erasable itemset (EI) mining, a branch of pattern mining, helps managers to establish new plans for the development of new products. Although the problem of mining EIs was first proposed in 2009, many efficient algorithms for mining these have since been developed. However, these algorithms usually require a lot of time and memory usage. In reality, users only need a small number of EIs which satisfy a particular condition. Having this observation in mind, in this study we develop an efficient algorithm for mining EIs with subset and superset itemset constraints (C0 ⊆ X ⊆ C1). Firstly, based on the MEI (Mining Erasable Itemsets) algorithm, we present the MEIC (Mining Erasable Itemsets with subset and superset itemset Constraints) algorithm in which each EI is checked with regard to the constraints before being added to the results. Next, two propositions supporting quick pruning of nodes that do not satisfy the constraints are established. Based on these, we propose an efficient algorithm for mining EIs with subset and superset itemset constraints (called pMEIC - p: pruning). The experimental results show that pMEIC outperforms MEIC in terms of mining time and memory usage.