Incrementally updating High-Utility itemsets with transaction insertion

Abstract

High-utility itemsets mining (HUIM) is designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of HUIM are designed to handle the static database. Fewer research handles the dynamic HUIM with transaction insertion, thus requiring the computations of database rescan and combination explosion of pattern-growth mechanism. In this paper, an efficient incremental algorithm with transaction insertion is designed to reduce computations without candidate generation based on the utility-list structures. The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, and memory consumption.