Cluster-weighted modeling

In data mining, cluster-weighted modeling (CWM) is an algorithm-based approach to non-linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld.

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enIn data mining, cluster-weighted modeling (CWM) is an algorithm-based approach to non-linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld.
Has abstract
enIn data mining, cluster-weighted modeling (CWM) is an algorithm-based approach to non-linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld.
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Cluster-weighted modeling
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enCluster-weighted modeling
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Bayesian analysis
Category:Cluster analysis algorithms
Category:Estimation of densities
Category:Multivariate statistics
Conditional expected value
Conditional probability distribution
Conditional variance
Data mining
Data transformation
Density estimation
Dependent and independent variables
Gaussian function
Joint probability distribution
Marginal distribution
Mixture model
Neil Gershenfeld
Normal distribution
Regression analysis
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4hbk3
Cluster-weighted modeling
m.05mzxys
Q5136674
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Category:Cluster analysis algorithms
Category:Estimation of densities
Category:Multivariate statistics
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