IEEE AccessTraditional pattern mining algorithms are based on tree and linked list structures. However, they often only consider a single factor of frequency or utility and have to deal with exponential search spaces as well as generate numerous candidates.
Xiumei Zhao, Xincheng Zhong, Bing Han
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A primer to frequent itemset mining for bioinformatics. [PDF]
Brief Bioinform, 2015 Naulaerts S +6 more
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FraudMiner: a novel credit card fraud detection model based on frequent itemset mining. [PDF]
ScientificWorldJournal, 2014 Seeja KR, Zareapoor M, Zareapoor M.
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Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques. [PDF]
Proteome Sci, 2014 Vu TN +6 more
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Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data. [PDF]
Eng Appl Artif Intell, 2015 Smart O, Burrell L.
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SiBIC: a web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining. [PDF]
PLoS One, 2013 Takahashi K, Takigawa I, Mamitsuka H.
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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]
Sci RepSinthuja M, Diviya M, Saranya P.
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Mining personalized core traditional Chinese medicine prescriptions for rheumatoid arthritis and elucidating their mechanisms via frequent closed Itemset compression and multilevel network pharmacology. [PDF]
Front Mol BiosciChen X +11 more
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Vehicle Trajectory Prediction Method for Task Offloading in Vehicular Edge Computing. [PDF]
Sensors (Basel), 2023 Yan R, Gu Y, Zhang Z, Jiao S.
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An evolutionary computation-based sensitive pattern hiding model under a multi-threshold constraint in healthcare. [PDF]
Sci RepSharma S, Sharma R, Kumar S, Min H.
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