Using Bayesian Networks For Visualizing High-Dimensional (original) (raw)

1999

Abstract

A Bayesian (belief) network is a representation of a probability distribution over a set of random variables. One of the main advantages of this model family is that it ooers a theoretically solid machine learning framework for constructing accurate domain models from sample data eeciently and reliably. As the parameters of a Bayesian network have a precise semantic interpretation, the learned models can be used for data mining purposes, i.e., for examining regularities found in the data. In addition to this type of direct examination of the model, we suggest that the learned Bayesian networks can also be used for indirect data mining purposes through a visualization scheme which can be used for producing 2D or 3D representations of high-dimensional problem domains. Our visualization scheme is based on the predictive distributions produced by the Bayesian network model, which means that the resulting visualizations can also be used as a post-processing tool for visual inspection of ...

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