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Data mining



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International Journal of Production Research Vol 44 Nos 18 19 15 September 1 October 2006 4175 4191 Data mining manufacturing and service applications A KUSIAK Intelligent Systems Laboratory Mechanical and Industrial Engineering 2139 Seamans Center The University of Iowa Iowa City Iowa 52242 1527 USA In this paper basic concepts of machine learning and data mining are introduced Machine learning algorithms extract knowledge from diverse data bases that can be used to build decision making systems For example based on the operational engineering data equipment faults can be detected the number of items to be ordered can be predicted optimal control parameters can be determined A framework for organizing and applying knowledge for decision making in manufacturing and service applications is presented The framework uses decision making constructs such decision tables decision maps and atlases It offers a new data driven paradigm of importance to modern manufacturing and service organisations Examples of data mining applications in industrial medical and pharmaceutical domains are presented It is envisioned that the data driven framework presented in the paper will enhance these applications Keywords Data mining Decision making Knowledge structuring Process modelling Industrial applications 1 Introduction The growing volume of enterprise data raises many challenges with one being able to extract store organize and use the knowledge generated from data sets The data content is frequently determined by the legacy systems deployed at various applications and time periods Understanding the meaning and structure of the stored information is difficult as the data is often heterogeneous and distributed The data can be modelled as continuous functions qualitative relations decisionmaking rules and so on The underlying physical biological or organizational principles dictate the form of heterogeneous models that most often interact with each other Despite this complex reality



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