Results 121 to 130 of about 2,811 (218)

Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-growth*

, 2008
. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model.
Marek Wojciechowski, Krzysztof Galecki, Krzysztof Gawronek   +2 more
core  

FREQUENT ITEMSETS MINING FOR BIG DATA

, 2019
Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an important role in Data Mining. It has a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Generally speaking, FIM counts the frequencies of co-occurrence items,
openaire   +2 more sources

Frequent Itemset Hiding Algorithm Using Frequent Pattern Tree Approach

, 2012
A problem that has been the focus of much recent research in privacy preserving data-mining is the frequent itemset hiding (FIH) problem. Identifying itemsets that appear together frequently in customer transactions is a common task in association rule ...
Alnatsheh, Rami H.
core  

FraudMiner: a novel credit card fraud detection model based on frequent itemset mining. [PDF]

ScientificWorldJournal, 2014
Seeja KR, Zareapoor M, Zareapoor M.
europepmc   +1 more source

Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques. [PDF]

Proteome Sci, 2014
Vu TN, Mrzic A, Valkenborg D, Maes E, Lemière F, Goethals B, Laukens K.   +6 more
europepmc   +1 more source

Improved Algorithm for Frequent Itemsets Mining Based on Apriori and FP-Tree

, 2013
Frequent itemset mining plays an important role in association rule mining.
Priti Chandra, B.L.Deekshatulu, Dr. Sujatha Dandu   +2 more
core  

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.
europepmc   +1 more source

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.
europepmc   +1 more source

Best-first search-based approach for mining top-k closed frequent itemsets from uncertain databases. [PDF]

PLoS One
Le N, Vo H, Nguyen T.
europepmc   +1 more source

CLTD-LP: an optimized top-down clustering approach with linear prefix trees for scalable frequent pattern discovery in large datasets. [PDF]

Sci Rep
Sinthuja M, Diviya M, Saranya P.
europepmc   +1 more source