Results 31 to 40 of about 2,346 (201)

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

ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware [PDF]

, 2019
Billions of users rely on the security of the Android platform to protect phones, tablets, and many different types of consumer electronics. While Android's permission model is well studied, the enforcement of the protection policy has received ...
Arzt Steven, Backes Michael, Backes Michael, Dalton Andrew, Enck William, Enck William, Felt Adrienne Porter, Grace Michael, Lam Patrick, McCoy Travis, Milian Mark, Ranger Steve, Shao Yuru, Spahn Riley, Tan Lin, Vallée-Rai Raja, Zhang Xiaolan   +16 more
core   +2 more sources

An efficient parallel method for mining frequent closed sequential patterns [PDF]

, 2017
Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining.
Huynh, Bao, Snášel, Václav, Vo, Bay
core   +1 more source

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

A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

Complex System Modeling and Simulation, 2023
High-utility itemset mining (HUIM) can consider not only the profit factor but also the profitable factor, which is an essential task in data mining. However, most HUIM algorithms are mainly developed on a single machine, which is inefficient for big ...
Zaihe Cheng, Wei Shen, Wei Fang, Jerry Chun-Wei Lin   +3 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

HI-Tree: Mining High Influence Patterns Using External and Internal Utility Values [PDF]

, 2015
We propose an efficient algorithm, called HI-Tree, for mining high influence patterns for an incremental dataset. In traditional pattern mining, one would find the complete set of patterns and then apply a post-pruning step to it.
C Ahmed, CKS Leung, CW Lin, GC Lan, H Yao, J Han, SK Tanbeer, Y Liu   +7 more
core   +1 more source

TargetUM: Targeted High-Utility Itemset Querying

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

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

Privacy Preserving Utility Mining: A Survey

, 2018
In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and ...
Chao, Han-Chieh, Gan, Wensheng, Lin, Jerry Chun-Wei, Wang, Shyue-Liang, Yu, Philip S.   +4 more
core   +1 more source