Results 41 to 50 of about 615 (220)
IEEE Access, 2018 High average-utility itemsets mining (HAUIM) is an emerging topic in data mining. Compared to traditional high utility itemset mining, HAUIM more fairly measures the utility of itemsets by considering their lengths (number of items).
Jerry Chun-Wei Lin +2 more
doaj +1 more source
FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining
IEEE Access, 2020 Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task.
Tianyou Wei +5 more
doaj +1 more source
A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
Applied Sciences, 2022 Compared to fuzzy utility itemset mining (FUIM), temporal fuzzy utility itemset mining (TFUIM) has been proposed and paid attention to in recent years.
Tzung-Pei Hong +5 more
doaj +1 more source
Engineering Reports, Volume 8, Issue 5, May 2026.Social media algorithms drive a hidden risk chain: over‐disclosure → behavioral fusion → targeted attacks. We propose a 128‐dim, law‐aware risk scoring model with Drools‐based dynamic alerts for universities. ABSTRACT As universities undergo accelerated digital transformation, social media algorithms—while streamlining campus services—have emerged as a
Weishu Ye, Zhi Li
wiley +1 more source
Generic Itemset Mining Based on Reinforcement Learning
IEEE Access, 2022 One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants to extract a different type of itemsets.
Kazuma Fujioka, Kimiaki Shirahama
doaj +1 more source
Reading Research Quarterly, Volume 61, Issue 2, April/May/June 2026.This study introduces and validates the Self‐Efficacy for Online Reading Questionnaire (SEORQ), a process‐grounded instrument designed to measure secondary students' efficacy in executing the core demands of online reading. The model conceptualizes online reading self‐efficacy as a multidimensional construct encompassing five interrelated processes ...
SeongYeup Kim +2 more
wiley +1 more source
Mining High Utility Itemsets Based on Pattern Growth without Candidate Generation
Mathematics, 2020 Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets.
Yiwei Liu, Le Wang, Lin Feng, Bo Jin
doaj +1 more source
Personalized and Explainable Aspect‐Based Recommendation Using Latent Opinion Groups
Computational Intelligence, Volume 42, Issue 2, April 2026.ABSTRACT The problem of explainable recommendation—supporting the recommendation of a product or service with an explanation of why the item is a good choice for the user—is attracting substantial research attention recently. Recommendations associated with an explanation of how the aspects of the chosen item may meet the needs and preferences of the ...
Maryam Mirzaei +2 more
wiley +1 more source
A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM
IEEE AccessFrequent itemset mining (FIM) and high utility itemset mining (HUIM) are popular data mining techniques used in various real-world applications such as retail-market, bio-medicine, and click-stream analysis.
Muhammad Waheed Ashraf +2 more
doaj +1 more source
EHAUPM: Efficient High Average-Utility Pattern Mining With Tighter Upper Bounds
IEEE Access, 2017 High-utility itemset mining (HUIM) has become a popular data mining task, as it can reveal patterns that have a high-utility, contrarily to frequent pattern mining, which focuses on discovering frequent patterns.
Jerry Chun-Wei Lin +3 more
doaj +1 more source