Rough set theory: a data mining tool for semiconductor manufacturing

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Abstract
The growing volume of information poses interesting challenges and calls for tools that discover properties of data. Data mining has emerged as a discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decisionmaking. In this paper, the basic concepts of rough set theory and other aspects of data mining are introduced. The rough set theory offers a viable approach for extraction of decision rules from data sets. The extracted rules can be used for making predictions in the semiconductor industry and other applications. This contrasts other approaches such as regression analysis and neural networks where a single model is built. One of the goals of data mining is to extract meaningful knowledge. The power, generality, accuracy, and longevity of decision rules can be increased by the application of concepts from systems engineering and evolutionary computation introduced in this paper. A new rule-structuring algorithm is proposed. The concepts presented in the paper are illustrated with examples.

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