Abstract
Compared to fuzzy utility itemset mining (FUIM), temporal fuzzy utility itemset mining (TFUIM) has been proposed and paid attention to in recent years. It considers the characteristics of transaction time, sold quantities of items, unit profit, and transformed semantic terms as essential factors. In the past, a tree-structure method with two phases was previously presented to solve this problem. However, it spent much time because of the number of candidates generated. This paper thus proposes a one-phase tree-structure method to find the high temporal fuzzy utility itemsets in a temporal database. The tree was designed to maintain candidate 1-itemsets with their upper bound values meeting the defined threshold constraint. Besides, each node in this tree keeps the required data of a 1-itemset for mining. We also designed an algorithm to construct the tree and gave an example to illustrate the mining process in detail. Computational experiments were conducted to demonstrate the one-phase tree-structure method is better than the previous one regarding the execution time on three real datasets.
Highlights
Frequent itemset mining (FIM) is one of the most popular data-mining problems
The problem uses a measure for itemsets, and those satisfying the utility threshold constraint are regarded as high utility itemsets
In order to solve the temporal fuzzy utility itemset mining (TFUIM) problem, a two-phase algorithm based on a tree structure was previously proposed to improve the performance efficiency in mining [18]
Summary
Tzung-Pei Hong 1,2, * , Cheng-Yu Lin 2 , Wei-Ming Huang 3 , Shu-Min Li 2 , Shyue-Liang Wang 1 and Jerry Chun-Wei Lin 4.
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