Discovering Partial Periodic High Utility Itemsets in Temporal Databases

摘要

High Utility Itemset Mining (HUIM) is an important model with many real-world applications. Given a (non-binary) transactional database and an external utility database, the aim of HUIM is to discover all itemsets within the data that satisfy the user-specified minimum utility (minUtil) constraint. The popular adoption and successful industrial application of HUIM has been hindered by the following two limitations: (ⅰ) HUIM does not allow external utilities of items to vary over time and (ⅱ) HUIM algorithms are inadequate to find recurring customer purchase behavior. This paper introduces a flexible model of Partial Periodic High Utility Itemset Mining (PPHUIM) to address these two problems. The goal of PPHUIM is to discover only those interesting high utility item-sets that are occurring at regular intervals in a given temporal database. An efficient depth-first search algorithm, called PPHUI-Miner (Partial Periodic High Utility Itemset-Miner), has been proposed to enumerate all partial periodic high-utility itemsets in temporal databases. Experimental results show that the proposed algorithm is efficient.