Data Mining

This clear and accessible introduction to the subject shows how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. It also covers subjects such as customer analysis in greater depth.

Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: • supply chain design, • product development, • manufacturing system design, • product quality control, and • preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.

Introduces data mining methods for the solution of design issues Presents methods for preprocessing data prior to mining Written by experts Includes supplementary material: sn.pub/extras

Autorentext

Yong Yin has been Associate Professor at Yamagata University, Japan, since 2004. He was previously Assistant Professor at the same university from 2002 to 2004. His research areas are manufacturing strategy; product development; workforce agility; and supply chain management.

Ikou Kaku is a professor at the Department of Management Science and Engineering, Akita Prefectural University, Japan. His research interests are in human factors related to manufacturing; mathematical modeling and meta heuristics; data mining techniques and their application in inventory management; and supply chain management.

Jiafu Tang is a professor at Northeastern University, Shenyang, China. He works in the Institute of Systems Engineering's Key Laboratory of Integrated Automation of Process Industry of MOE.

JianMing Zhu is a professor at the Central University of Finance and Economics, Beijing, China. He works in the School of Information.

Klappentext

Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems.

Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including:
• market segmentation,
• product design,
• organization design,
• manufacturing design, and
• supply chain design.

Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.

Inhalt

1. Decision Analysis and Cluster Analysis.- 2. Association Rules Mining in Inventory Data Base.- 3. Fuzzy Modeling and Optimization: Theory and Methods.- 4. Genetic Algorithm Based Fuzzy Nonlinear Programming.- 5. Neural Network and Self Organizing Maps.- 6. Privacy Preserving Data Mining.- 7. Supply Chain Design by Using Decision Analysis.- 8. Product Architecture and Product Development Process for Global Performance.- 9. Application of Cluster Analysis to Cellular Manufacturing.- 10. Manufacturing Cells Design by Cluster Analysis.- 11. Fuzzy Approach to Quality Function Deployment-based Product Planning.- 12. Decision Making with Consideration of Association in Supply Chains.- 13. Applying Self Organizing Maps to Master Data Making in Automatic Exterior Inspection.- 14. Application for Privacy Preserving Data Mining.