title: An incremental high-utility mining algorithm with transaction insertion.
abstract: Association-rule mining is commonly used to discover useful and meaningful patterns from a very large database. It only considers the occurrence frequencies of items to reveal the relationships among itemsets. Traditional association-rule mining is, however, not suitable in real-world applications since the purchased items from a customer may have various factors, such as profit or quantity. High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures. Most algorithms of high-utility mining are designed to handle the static database. Fewer researches handle the dynamic high-utility mining 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 the computations. Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.
authors: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus, Shenzhen University Town, Xili, Shenzhen 518055, China.
authors: Jerry Chun-Wei
authors: 0000-0001-8768-9709
authors: JC
authors: Lin
authors: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus, Shenzhen University Town, Xili, Shenzhen 518055, China.
authors: Wensheng
authors: W
authors: Gan
authors: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan ; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
authors: Tzung-Pei
authors: 0000-0001-7305-6492
authors: TP
authors: Hong
authors: Medical School, Shenzhen University, Shenzhen 518060, China.
authors: Binbin
authors: B
authors: Zhang