Results 11 to 20 of about 498 (210)
Proof Learning in PVS With Utility Pattern Mining [PDF]
IEEE Access, 2020 Interactive theorem provers (ITPs) are software tools that allow human users to write and verify formal proofs. In recent years, an emerging research area in ITPs is proof mining, which consists of identifying interesting proof patterns that can be used ...
M. Saqib Nawaz +2 more
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Approximate Parallel High Utility Itemset Mining [PDF]
Big Data Research, 2016 High utility itemset mining discovers itemsets whose utility is above a given threshold, where the utility measures the importance of an itemset. It overcomes the limitation of frequent pattern mining, which uses frequency as its quality measure. To speed up the performance for mining high utility itemsets, many algorithms have been proposed which ...
Yan Chen 0021, Aijun An
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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop [PDF]
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 +3 more
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An Efficient Approach for Mining High Average-Utility Itemsets in Incremental Database
Engineering ProceedingsTraditional high-utility itemset (HUI) mining methods tend to overestimate utility for long itemsets, leading to biased results. High average-utility itemset (HAUI) mining addresses this problem by normalizing utility with itemset length.
Ye-In Chang +2 more
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TKU-PSO: An Efficient Particle Swarm Optimization Model for Top-K High-Utility Itemset Mining. [PDF]
International Journal of Interactive Multimedia and Artificial IntelligenceTop-k high-utility itemset mining (top- HUIM) is a data mining procedure used to identify the most valuable patterns within transactional data. Although many algorithms are proposed for this purpose, they require substantial execution times when the ...
Simen Carstensen, Jerry Chun Wei Lin
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FHUQI-Miner: Fast high utility quantitative itemset mining [PDF]
, 2021 High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task is useful to understand customer behavior, an important limitation is that high
Nouioua, Mourad +4 more
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Behavior Decoding Delineates Seizure Microfeatures and Associated Sudden Death Risks in Mouse Models of Epilepsy. [PDF]
Ann NeurolObjective Behavior and motor manifestations are distinctive yet often overlooked features of epileptic seizures. Seizures can result in transient disruptions in motor control, often organized into specific behavioral sequences that can inform seizure types, onset zones, and outcomes.
Shen Y +8 more
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A review on big data based parallel and distributed approaches of pattern mining
Journal of King Saud University: Computer and Information Sciences, 2022 Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility
Sunil Kumar, Krishna Kumar Mohbey
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EAHUIM: Enhanced Absolute High Utility Itemset Miner for Big Data
International Journal of Information Management Data Insights, 2022 High utility itemset mining (HUIM) is a data mining technique that identifies the itemsets with utility levels exceeding a pre-determined threshold. The factor utility is described as the combination of magnitude and element of significance for an item ...
Vandna Dahiya, Sandeep Dalal
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High-utility Itemsets Mining Algorithm Based on Double Binary Particle Swarm Optimization [PDF]
Jisuanji gongcheng, 2018 High-utility itemset mining algorithm is an important part of association analysis.By improving the basic binary particle swarm optimization algorithm,a Double Binary Particle Swarm Optimization（DBPSO） algorithm is proposed.The minimum utility threshold ...
JIN Xiaole,LIU Xiabi,MA Xiao
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