Knowledge Discovery for Support Structure Type Selection of Thrust Bearing Using Bayesian Decision Based on Rough Set

A new method of Bayesian decision based on rough set is proposed in order to mine tacit knowledge and latent rules in support structure type selection of thrust bearing. Firstly, rough set theory is applied to reduce all factors considered in type selection for getting determinative factors. By a he...

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Bibliographic Details
Main Authors: Wei Guo, Xin Song
Format: Conference Proceeding
Language:English
Subjects:
Bayesian decision
Bayesian methods
Data mining
Fuzzy systems
Heuristic algorithms
Information systems
Mechanical engineering
Mutual information
Probability
Rough set
Set theory
Statistics
Support structure
Trust bearing
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Summary:A new method of Bayesian decision based on rough set is proposed in order to mine tacit knowledge and latent rules in support structure type selection of thrust bearing. Firstly, rough set theory is applied to reduce all factors considered in type selection for getting determinative factors. By a heuristic algorithm based on improved mutual information, the minimal attributes reduction is obtained and makes up of decision table with decision attributes. Then according to the decision table, Bayesian decision with minimal risk is employed to extract decision rules. In this paper, the concise decision rules are extracted from representative cases and evaluation is made in some successful cases. Experiment results show that it is feasible and effective to use the method to knowledge discovery for support structure type selection of thrust bearing.
DOI:10.1109/FSKD.2008.102