Data mining: manufacturing and service applications

Abstract

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