Affiliations: [a] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| [b] Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
| [c] Department of Computer Engineering, Sejong University, Seoul, Korea
| [d] Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan
Correspondence:
[*]
Corresponding author. Jerry Chun-Wei Lin, Department of Computer Science, Electrical Engineering
and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway.
E-mail: jerrylin@ieee.org.
Abstract: HAUIM (High Average-Utility Itemset Mining) is a variation of HUIM (High-Utility Itemset Mining) that provides a reliable measure to reveal utility patterns in light of the length of the mined pattern. Several works have been studied to improve mining efficiency by designing multiple pruning strategies and efficient frameworks, but fewer studies have centered on the sophisticated database maintenance algorithm. Existing works still have to rescan the databases multiple times when it is necessary. We first use the pre-large principle in this paper to efficiently update the newly discovered HAUIs. For further updates and maintenance on the basis of the two thresholds, the Pre-large Average Utility Itemset (PAUI) can be maintained to increase the mining performance. Experiments will then be performed to compare the batch model, the Fast-Updated (FUP)-based model, and the Apriori-like HAUIM (APHAUIM) model designed in respect of the number of maintenance patterns, scalability, runtime, and memory usage.
