Titlebook: Data Mining and Knowledge Discovery for Process Monitoring and Control; Xue Z. Wang Book 1999 Springer-Verlag London 1999 Artificial Intel

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978-1-4471-1137-5Springer-Verlag London 1999

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Data Mining and Knowledge Discovery for Process Monitoring and Control978-1-4471-0421-6Series ISSN 1430-9491 Series E-ISSN 2193-1577

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Mustafa A. Al-Asadi,Sakir TasdemirThis chapter describes some representative unsupervised machine learning approaches for process operational state identification. Whether a machine learning approach is regarded as supervised or unsupervised depends on the way it makes use of prior knowledge of the data. Data encountered can be broadly divided into the following four categories:

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Ivana Cvitanovi?,Marina Bagi? Babac systems. As distinguished from similarity or distance based clustering, such conceptual clustering is able to generate conceptual knowledge about the major variables which are responsible for clustering, as well as predicting operational states. The resulting knowledge is expressed in the form of production rules or decision trees.

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Inductive Learning for Conceptual Clustering and Real-Time Process Monitoring, systems. As distinguished from similarity or distance based clustering, such conceptual clustering is able to generate conceptual knowledge about the major variables which are responsible for clustering, as well as predicting operational states. The resulting knowledge is expressed in the form of production rules or decision trees.

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1430-9491 tic generation of decision trees and production rules from dModern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with