A Bayesian approach to clustering and classification

The author describes a classification approach and associated algorithms designed for use with continuous but non-Gaussian data. The probability density function for each class is modeled as a mixture of Gaussian distributions. The clustering algorithm estimates the means the covariances of the comp...

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Bibliographic Details
Main Author: Laskey, K.B.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Classification tree analysis
Clustering algorithms
Data engineering
Decision trees
Design engineering
Gaussian distribution
Linear discriminant analysis
Piecewise linear techniques
Space technology
Online Access:Request full text
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Summary:The author describes a classification approach and associated algorithms designed for use with continuous but non-Gaussian data. The probability density function for each class is modeled as a mixture of Gaussian distributions. The clustering algorithm estimates the means the covariances of the component Gaussian distributions for each class. A classification rule based on the mixture model is presented.< >
DOI:10.1109/ICSMC.1991.169681