Intelligent Data Mining: Techniques and Applications (Studies in Computational Intelligence, 5)

ISBN 10: 3540262563 / ISBN 13: 9783540262565

Editore: Springer August 2005, 2005

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A square, tight and unmarked copy with text which appears unread. NOT an ex-libre copy. Codice articolo 49698

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Titolo: Intelligent Data Mining: Techniques and ...

Casa editrice: Springer August 2005

Data di pubblicazione: 2005

Legatura: Hardcover

Condizione: Used - fine

Descrizione articolo

Riassunto:

"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.

Contenuti:

From the contents: Part 1: Intelligent Systems and Data Mining; Some Considerations in Multi-Source Data Fusion; Granular Nested Causal Complexes; Gene Regulating Network Discovery; Semantic Relations and Information Discovery; Sequential Pattern Mining; Uncertain Knowledge Association through Information Gain; Data Mining for Maximal Frequency Patterns in Sequence Group; Mining Association Rule with Rough Sets; The Evolution of the Concept of Fuzzy Measure.- Part 2: Economic and Management Applications; Building ER Models with Association Rules; Discovering the Factors Affecting the Location Selection of FDI in China; Penalty-Reward Analysis with Uninorms: A Study of Customer (Dis)Satisfaction.- Part 3: Industrial Engineering Applications; Fuzzy Process Control with Intelligent Data Mining; Accelerating the New Product Introduction with Intelligent Data Mining; Integrated Clustering Modeling with Backpropagation Neural Network for Efficient Customer Relationship Management Mining.