Results 31 to 40 of about 905 (211)

An Efficient Approach for Mining Reliable High Utility Patterns

IEEE Access, 2022
Utility mining is one of the most thriving research topics with a wide range of real-world applications. High utility pattern mining uses a utility function to extract all desired patterns that exceed a minimum utility threshold.
Mohammed A. Fouad, Wedad Hussein, Sherine Rady, Philip S. Yu, Tarek F. Gharib   +4 more
doaj   +1 more source

High Average-Utility Itemset Sampling Under Length Constraints

, 2022
International audienceHigh Utility Itemset extraction algorithms are methods for discovering knowledge in a database where the items are weighted. Their usefulness has been widely demonstrated in many real world applications.
Diop, Lamine
core   +1 more source

LUIM: New Low-Utility Itemset Mining Framework

IEEE Access, 2019
High-utility itemset mining (HUIM), which is the detection of high-utility itemsets (HUIs) in a transactional database, provides the decision maker with greater flexibility to exploit item utilities, such as quantity and profits, to extract remarkable ...
Naji Alhusaini, Saleem Karmoshi, Ammar Hawbani, Li Jing, Abdullah Alhusaini, Yaser Al-sharabi   +5 more
doaj   +1 more source

CLS-Miner: efficient and effective closed high-utility itemset mining [PDF]

, 2018
High-utility itemset mining (HUIM) is a popular data mining task with applications in numerous domains. However, traditional HUIM algorithms often produce a very large set of high-utility itemsets (HUIs).
Quang-Huy Duong, Philippe Fournier-Viger, Thu-Lan Dam, Dam, Thu-Lan, Kenli Li, Li, Kenli, Duong, Quang-Huy, Fournier-Viger, Philippe   +7 more
core   +2 more sources

TargetUM: Targeted High-Utility Itemset Querying

CoRR, 2021
Preprint.
Jinbao Miao, Shicheng Wan, Wensheng Gan, Jiayi Sun 0002, Jiahui Chen 0002   +4 more
openaire   +2 more sources

Mining Correlated High Utility Itemsets in One Phase

IEEE Access, 2020
High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM
Bay Vo, Le V. Nguyen, Vinh V. Vu, Mi T. H. Lam, Thuy T. M. Duong, Ly T. Manh, Thuy T. T. Nguyen, Loan T. T. Nguyen, Tzung-Pei Hong   +8 more
doaj   +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

Actionable high-coherent-utility fuzzy itemset mining

, 2016
Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from quantitative transaction databases.
Chen, C. H.;Li, A. F.;Lee, Y. C., Chen, Chun-hao   +1 more
core   +1 more source

A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM

IEEE Access
Frequent 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, M. Asif Naeem, Heejeong Jasmine Lee   +2 more
doaj   +1 more source

A survey of itemset mining

, 2017
Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear ...
Jerry Chun‐Wei Lin, Vo, Bay, Bay Vo, Tin Truong Chi, Zhang, Ji, Philippe Fournier‐Viger, Hoai Bac Le, Chi, Tin Truong, Le, Hoai Bac, Lin, Jerry Chun-Wei, Ji Zhang, Fournier-Viger, Philippe   +11 more
core   +1 more source