Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts [PDF]
Sensors, 2022 Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies.
Saif Ur Rehman +4 more
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Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data
IEEE Access, 2018 Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy ...
Xinyu Xiong +6 more
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Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree
IEEE Access, 2020 Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on.
Jun-Feng Qu +5 more
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Incremental Frequent Itemsets Mining With FCFP Tree
IEEE Access, 2019 Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression ...
Jiaojiao Sun +3 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
europepmc +2 more sources
Efficiently Mining Frequent Itemsets on Massive Data
IEEE Access, 2019 Frequent itemset mining is an important operation to return all itemsets in the transaction table, which occur as a subset of at least a specified fraction of the transactions.
Xixian Han +5 more
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CLTD-LP: an optimized top-down clustering approach with linear prefix trees for scalable frequent pattern discovery in large datasets [PDF]
Scientific ReportsThe extraction of frequent itemsets and association rules is a fundamental challenge in data mining and holds significant importance within the field. Mining techniques utilising Linear Prefix (LP) growth association rules employ a bottom-up methodology ...
M. Sinthuja, M. Diviya, P. Saranya
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Mining frequent itemsets from streaming transaction data using genetic algorithms
Journal of Big Data, 2020 This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine.
Sikha Bagui, Patrick Stanley
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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
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A Parallel Apriori Algorithm and FP- Growth Based on SPARK [PDF]
ITM Web of Conferences, 2021 Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets.
Gupta Priyanka, Sawant Vinaya
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