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.
Read more about Cluster-weighted Modeling: Basic Form of Model, General Versions
Famous quotes containing the word modeling:
“The computer takes up where psychoanalysis left off. It takes the ideas of a decentered self and makes it more concrete by modeling mind as a multiprocessing machine.”
—Sherry Turkle (b. 1948)