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Temporal Fuzzy Utility Maximization with Remaining Measure by Wan, Shicheng; Ye, Zhenqiang; Gan, Wensheng; Chen, Jiahui is a scholarly article available to read on EtoBox.

What is Temporal Fuzzy Utility Maximization with Remaining Measure about?

High utility itemset mining approaches discover hidden patterns from large amounts of temporal data. However, an inescapable problem of high utility itemset mining is that its discovered results hide the quantities of patterns, which causes poor interpretability. The results only reflect the shopping trends of customers, which cannot help decision makers quantify collected information. In linguistic terms, computers use mathematical or programming languages that are precisely formalized, but the language used by humans is always ambiguous. In this paper, we propose a novel one-phase temporal fuzzy utility itemset mining approach called TFUM. It revises temporal fuzzy-lists to maintain less but major information about potential high temporal fuzzy utility itemsets in memory, and then discovers a complete set of real interesting patterns in a short time. In particular, the remaining measure is the first adopted in the temporal fuzzy utility itemset mining domain in this paper. The remaining maximal temporal fuzzy utility is a tighter and stronger upper bound than that of previous studies adopted. Hence, it plays an important role in pruning the search space in TFUM. Finally, we also

Author

Wan, Shicheng; Ye, Zhenqiang; Gan, Wensheng; Chen, Jiahui

Published

2022

Language

EN