  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 evaluate the efficiency and
effectiveness of TFUM on various datasets. Extensive experimental results
indicate that TFUM outperforms the state-of-the-art algorithms in terms of
runtime cost, memory usage, and scalability. In addition, experiments prove
that the remaining measure can significantly prune unnecessary candidates
during mining.
