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New variational Bayesian approaches for statistical data mi..:

Wu, Burton
https://eprints.qut.edu.au/46084/1/Burton_Wu_Thesis.pdf. , 2011

RT Journal
T1 <Title>New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftqueensland:oai:eprints.qut.edu.au:46084&Exemplar=1&LAN=DE
				
A1 Wu, Burton
PB Queensland University of Technology
YR 2011
K1 Gaussian mixture model (GMM)
K1 mixture models
K1 probability density estimation
K1 variational bayes (VB)
K1 Bayesian statistics
K1 data mining (DM)
K1 combinational data analysis (CDA)
K1 profiling
K1 segmentation
K1 clustering
K1 feature extraction
K1 behavioural characteristics
K1 consumer behaviour
K1 customer behaviour
K1 consumption behaviour
K1 customer relationship management (CRM)
K1 relationship marketing (RM)
K1 human mobility pattern
K1 spatial behaviour
K1 temporal behaviour
K1 circular data
K1 data stream
K1 high dimensional data
K1 call detail records (CDR)
K1 wireless telecommunication industry
JF https://eprints.qut.edu.au/46084/1/Burton_Wu_Thesis.pdf
LK http://dx.doi.org/https://eprints.qut.edu.au/46084/
DO https://eprints.qut.edu.au/46084/
SF ELIB - SuUB Bremen

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