# Bayesian Classification (AutoClass): Theory and Results

@inproceedings{Cheeseman1996BayesianC, title={Bayesian Classification (AutoClass): Theory and Results}, author={Peter C. Cheeseman and John C. Stutz}, booktitle={Advances in Knowledge Discovery and Data Mining}, year={1996} }

We describe AutoClass an approach to unsupervised classi cation based upon the classical mixture model supplemented by a Bayesian method for determining the optimal classes We include a moderately detailed exposition of the mathematics behind the AutoClass system We emphasize that no current unsupervised classi cation system can produce maximally useful results when operated alone It is the interaction between domain experts and the machine searching over the model space that generates new…

## 1,303 Citations

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### AutoClass@IJM: a powerful tool for Bayesian classification of heterogeneous data in biology

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This work developed AutoClass@IJM, a computational resource with a web interface to AutoClass, a powerful unsupervised Bayesian classification system developed by the Ames Research Center at N.A.S.A., which has many powerful features with broad applications in biological sciences.

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### Case studies: Public domain, single mining tasks systems: autoclass (clustering)

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AutoClass seeks intrinsic clusters or classes in an instance vector database. It applies a user-specified probabilistic class model and searches for a maximum posterior probability parameterization…

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### USING BAYESIAN CLASSIFICATION FOR AQ-BASED LEARNING WITH CONSTRUCTIVE INDUCTION

- Computer Science
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### AutoClassWrapper: a Python wrapper for AutoClass C classification

- Computer ScienceJ. Open Source Softw.
- 2019

In proteomics and genomics, where thousands of proteins or genes are detected at once, the need for data classification is even more crucial, and AutoClass@IJM, a web server that utilizes AutoClass C, has made Bayesian classification more accessible.

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A heuristic method for learning mixtures of Bayesian Networks (MBNs) from possibly incomplete data in which each mixture component is a Bayesian network encoding a conditional Gaussian distribution over a set of variables.

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