Intelligent Data Mining Techniques and Applications /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Ruan, Da (Editor), Chen, Guoqing (Editor), Kerre, Etienne E. (Editor), Wets, Geert (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
• Series: Studies in Computational Intelligence, 5
• Subjects: Engineering.; Information technology.; Business > Data processing.; Computers.; Pattern recognition.; Applied mathematics.; Engineering mathematics.; Control engineering.; Robotics.; Mechatronics.; Appl.Mathematics/Computational Methods of Engineering.; Pattern Recognition.; Information Systems and Communication Service.; IT in Business.; Control, Robotics, Mechatronics.

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 object...

Table of Contents:

• 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.