Frequent Itemset Mining using QUBO
, 2019 In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.
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Application of K-means supported by clustered systems in big data association rule mining
Systems and Soft Computings: Association rule mining plays an important role in the field of data mining, which is used to discover hidden relationships. However, as data volumes increase, traditional association rule mining methods are constrained to single-machine computing ...
Lihua Liu
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Mining Assocation Rules Using Frequent Closed Itemsets
, 2005 In the domain of knowledge discovery in databases and its computational part called data mining, many works addressed the problem of association rule extraction that aims at discovering relationships between sets of items (binary attributes). An example association rule fitting in the context of market basket data analysis is cereal Ù milk ® sugar ...
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DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining
MathematicsTraditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources.
Mohsin Shaikh +4 more
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A primer to frequent itemset mining for bioinformatics. [PDF]
Brief Bioinform, 2015 Naulaerts S +6 more
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Frequent itemset mining on multiprocessor systems
, 2014 Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data.
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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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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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