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Intelligent Data Mining: Techniques and Applications (Studies in Computational Intelligence)

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"Intelligent Data Mining – Techniques and Applications" is an organized edited collection of contributed chapters covering basic knowledge for intelligent systems and data mining, applications in economic and management, industrial engineering and other related industrial applications. The main objective of this book is to gather a number of peer-reviewed high quality contributions in the relevant topic areas. The focus is especially on those chapters that provide theoretical/analytical solutions to the problems of real interest in intelligent techniques possibly combined with other traditional tools, for data mining and the corresponding applications to engineers and managers of different industrial sectors. Academic and applied researchers and research students working on data mining can also directly benefit from this book.

In today’s information-driven economy, companies may benefit a lot from suitable information management. Although information management is not just a technology-based concept rather a business practice in general, the possible and even indispensable support of IT-tools in this context is obvious. Because of the large data repositories many firms maintain nowadays, an important role is played by data mining techniques that find hidden, non-trivial, and potentially useful information from massive data sources. The discovered knowledge can then be further processed in desired forms to support business and scientific decision making.

Data mining (DM) is also known as Knowledge Discovery in Databases Following a formal definition by W. Frawley, G. Piatetsky-Shapiro and C. Matheus (in AI Magazine, Fall 1992, pp. 213–228), DM has been defined as “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data.” It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form that is easily comprehensible to humans. Since the middle of 1990s, DM has been developed as one of the hot research topics within computer sciences, AI and other related fields. More and more industrial applications of DM have been recently realized in today’s IT time.
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