Discovery of high utility itemsets from on-shelf time periods of products

Utility mining has recently been an emerging topic in the field of data mining. It finds out high utility itemsets by considering both the profits and quantities of items in transactions. It may have a bias if items are not always on shelf. In this paper, we thus design a new kind of patterns, named high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. We also propose a two-phased mining algorithm to effectively and efficiently discover high on-shelf utility itemsets. In the first phase, the possible candidate on-shelf utility itemsets within each time period are found level by level. In the second phase, the candidate on-shelf utility itemsets are further checked for their actual utility values by an additional database scan. At last, the experimental results on synthetic datasets also show the proposed approach has a good performance.