Affiliations: [a] Innovative Information Industry Research Center, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Shenzhen, Guangdong, China | [b] Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Shenzhen, Guangdong, China | [c] Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan | [d] Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan | [e] Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
Correspondence:
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Corresponding author: Guo-Cheng Lan, Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan. E-mail: [email protected].
Abstract: Association-rule mining is used to mine the relationships among the occurrences itemsets in a transactional database. An item is treated as a binary variable whose value is one if it appears in a transaction and zero otherwise. In real-world applications, several products may be purchased at the same time, with each product having an associated profit, quantity, and price. Association-rule mining from a binary database is thus not sufficient in some applications. Utility mining was thus proposed as an extension of frequent-itemset mining for considering various factors from the user. Most utility mining approaches can only process static databases and use batch processing. In real-world applications, transactions are dynamically inserted into or deleted from databases. The Fast UPdated (FUP) algorithm and the FUP2 algorithm were respectively proposed to handle transaction insertion and deletion in dynamic databases. In this paper, a fast-updated high-utility itemsets for transaction deletion (FUP-HUI-DEL) algorithm is proposed to handle transaction deletion for efficiently updating discovered high utility itemsets in decremental mining. The two-phase approach in high utility mining is applied to the proposed FUP-HUI-DEL algorithm for preserving the downward closure property to reduce the number of candidates. The FUP2 algorithm for handling transaction deletion in association-rule mining is adopted in the proposed FUP-HUI-DEL algorithm to reduce the number of scans of the original database in high utility mining. Experiments show that the proposed FUP-HUI-DEL algorithm outperforms the batch two-phase approach.
