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E-grāmata: Data Mining: Concepts, Methods and Applications in Management and Engineering Design

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  • Formāts: PDF+DRM
  • Sērija : Decision Engineering
  • Izdošanas datums: 16-Mar-2011
  • Izdevniecība: Springer London Ltd
  • Valoda: eng
  • ISBN-13: 9781849963381
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Data Mining: Concepts, Methods and Applications in Management and Engineering DesignPalielināt
  • Formāts: PDF+DRM
  • Sērija : Decision Engineering
  • Izdošanas datums: 16-Mar-2011
  • Izdevniecība: Springer London Ltd
  • Valoda: eng
  • ISBN-13: 9781849963381
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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.

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.

Recenzijas

From the reviews:

The book is a combination of a textbook and a collection of papers. useful for industrial and managerial practitioners who want to understand DM-related methods and how they could use DM to support their decisions. Beginning researchers might also benefit because the book reflects the diversity of DM methods without providing complex details of the algorithms. this book will inspire and motivate decision makers to consider DM as a useful approach for solving some decision problems. (Robert Stahlbock, Interfaces, Vol. 42 (4), July-August, 2012)

The authors spend the first six chapters of this book introducing the various methods of data analysis, focusing on programming algorithms for data mining related to the fields of business management and engineering design. Each chapter ends with a number of references that provide additional resources for those who wish to explore the issues further. I recommend this book to practitioners and researchers who are interested in data mining applications for business management and engineering design. (E. Y. Lee, ACM Computing Reviews, June, 2011)

1 Decision Analysis and Cluster Analysis 1(8)
1.1 Decision Tree
1(3)
1.2 Cluster Analysis
4(4)
References
8(1)
2 Association Rules Mining in Inventory Database 9(16)
2.1 Introduction
9(2)
2.2 Basic Concepts of Association Rule
11(3)
2.3 Mining Association Rules
14(3)
2.3.1 The Apriori Algorithm: Searching Frequent Itemsets
14(2)
2.3.2 Generating Association Rules from Frequent Itemsets
16(1)
2.4 Related Studies on Mining Association Rules in Inventory Database
17(5)
2.4.1 Mining Multidimensional Association Rules from Relational Databases
17(2)
2.4.2 Mining Association Rules with Time-window
19(3)
2.5 Summary
22(1)
References
23(2)
3 Fuzzy Modeling and Optimization: Theory and Methods 25(30)
3.1 Introduction
25(2)
3.2 Basic Terminology and Definition
27(2)
3.2.1 Definition of Fuzzy Sets
27(1)
3.2.2 Support and Cut Set
28(1)
3.2.3 Convexity and Concavity
28(1)
3.3 Operations and Properties for Generally Used Fuzzy Numbers
29(4)
3.3.1 Fuzzy Inequality with Tolerance
29(1)
3.3.2 Interval Numbers
30(1)
3.3.3 L–R Type Fuzzy Number
31(1)
3.3.4 Triangular Type Fuzzy Number
31(1)
3.3.5 Trapezoidal Fuzzy Numbers
32(1)
3.4 Fuzzy Modeling and Fuzzy Optimization
33(2)
3.5 Classification of a Fuzzy Optimization Problem
35(5)
3.5.1 Classification of the Fuzzy Extreme Problems
35(1)
3.5.2 Classification of the Fuzzy Mathematical Programming Problems
36(3)
3.5.3 Classification of the Fuzzy Linear Programming Problems
39(1)
3.6 Brief Summary of Solution Methods for FOP
40(11)
3.6.1 Symmetric Approaches Based on Fuzzy Decision
41(2)
3.6.2 Symmetric Approach Based on Non-dominated Alternatives
43(1)
3.6.3 Asymmetric Approaches
43(3)
3.6.4 Possibility and Necessity Measure-based Approaches
46(1)
3.6.5 Asymmetric Approaches to PMP5 and PMP6
47(2)
3.6.6 Symmetric Approaches to the PMP7
49(1)
3.6.7 Interactive Satisfying Solution Approach
49(1)
3.6.8 Generalized Approach by Angelov
50(1)
3.6.9 Fuzzy Genetic Algorithm
50(1)
3.6.10 Genetic-based Fuzzy Optimal Solution Method
51(1)
3.6.11 Penalty Function-based Approach
51(1)
References
51(4)
4 Genetic Algorithm-based Fuzzy Nonlinear Programming 55(32)
4.1 GA-based Interactive Approach for QP Problems with Fuzzy Objective and Resources
55(11)
4.1.1 Introduction
55(1)
4.1.2 Quadratic Programming Problems with Fuzzy Objective/Resource Constraints
56(3)
4.1.3 Fuzzy Optimal Solution and Best Balance Degree
59(1)
4.1.4 A Genetic Algorithm with Mutation Along the Weighted Gradient Direction
60(2)
4.1.5 Human—Computer Interactive Procedure
62(2)
4.1.6 A Numerical Illustration and Simulation Results
64(2)
4.2 Nonlinear Programming Problems with Fuzzy Objective and Resources
66(10)
4.2.1 Introduction
66(1)
4.2.2 Formulation of NLP Problems with Fuzzy Objective/Resource Constraints
67(3)
4.2.3 Inexact Approach Based on GA to Solve FO/RNP-1
70(2)
4.2.4 Overall Procedure for FO/RNP by Means of Human—Computer Interaction
72(2)
4.2.5 Numerical Results and Analysis
74(2)
4.3 A Non-symmetric Model for Fuzzy NLP Problems with Penalty Coefficients
76(9)
4.3.1 Introduction
76(1)
4.3.2 Formulation of Fuzzy Nonlinear Programming Problems with Penalty Coefficients
76(3)
4.3.3 Fuzzy Feasible Domain and Fuzzy Optimal Solution Set
79(1)
4.3.4 Satisfying Solution and Crisp Optimal Solution
80(3)
4.3,5 General Scheme to Implement the FNLP-PC Model
83(1)
4.3.6 Numerical Illustration and Analysis
84(1)
4.4 Concluding Remarks
85(1)
References
86(1)
5 Neural Network and Self-organizing Maps 87(14)
5.1 Introduction
87(2)
5.2 The Basic Concept of Self-organizing Map
89(3)
5.3 The Trial Discussion on Convergence of SOM
92(4)
5.4 Numerical Example
96(4)
5.5 Conclusion
100(1)
References
100(1)
6 Privacy-preserving Data Mining 101(20)
6.1 Introduction
101(3)
6.2 Security, Privacy and Data Mining
104(5)
6.2.1 Security
104(1)
6.2.2 Privacy
105(2)
6.2.3 Data Mining
107(2)
6.3 Foundation of PPDM
109(5)
6.3.1 The Characters of PPDM
109(1)
6.3.2 Classification of PPDM Techniques
110(4)
6.4 The Collusion Behaviors in PPDM
114(4)
6.5 Summary
118(1)
References
118(3)
7 Supply Chain Design Using Decision Analysis 121(12)
7.1 Introduction
121(2)
7,2 Literature Review
123(1)
7,3 The Model
124(3)
7.4 Comparative Statics
127(4)
7.5 Conclusion
131(1)
References
131(2)
8 Product Architecture and Product Development Process for Global Performance 133(24)
8.1 Introduction and Literature Review
133(3)
8.2 The Research Problem
136(4)
8.3 The Models
140(6)
8.3.1 Two-function Products
140(2)
8.3.2 Three-function Products
142(4)
8.4 Comparisons and Implications
146(6)
8.4.1 Three-function Products with Two Interfaces
146(1)
8.4.2 Three-function Products with Three Interfaces
146(5)
8.4.3 Implications
151(1)
8.5 A Summary of the Model
152(2)
8.6 Conclusion
154(1)
References
154(3)
9 Application of Cluster Analysis to Cellular Manufacturing 157(50)
9.1 Introduction
157(3)
9.2 Background
160(1)
9.2.1 Machine-part Cell Formation
160(1)
9.2.2 Similarity Coefficient Methods (SCM)
161(1)
9.3 Why Present a Taxonomy on Similarity Coefficients?
161(4)
9.3.1 Past Review Studies on SCM
162(1)
9.3.2 Objective of this Study
162(1)
9.3.3 Why SCM Are More Flexible
163(2)
9.4 Taxonomy for Similarity Coefficients Employed in Cellular Manufacturing
165(4)
9.5 Mapping SCM Studies onto the Taxonomy
169(7)
9.6 General Discussion
176(4)
9.6.1 Production Information-based Similarity Coefficients
176(3)
9.6.2 Historical Evolution of Similarity Coefficients
179(1)
9.7 Comparative Study of Similarity Coefficients
180(2)
9.7.1 Objective
180(1)
9.7.2 Previous Comparative Studies
181(1)
9.8 Experimental Design
182(9)
9.8.1 Tested Similarity Coefficients
182(1)
9.8.2 Datasets
183(4)
9.8.3 Clustering Procedure
187(1)
9.8.4 Performance Measures
188(3)
9.9 Comparison and Results
191(6)
9.10 Conclusions
197(1)
References
198(9)
10 Manufacturing Cells Design by Cluster Analysis 207(26)
10.1 Introduction
207(2)
10.2 Background, Difficulty and Objective of this Study
209(4)
10.2.1 Background
209(2)
10.2.2 Objective of this Study and Drawbacks of Previous Research
211(2)
10.3 Problem Formulation
213(8)
10.3.1 Nomenclature
213(2)
10.3.2 Generalized Similarity Coefficient
215(1)
10.3.3 Definition of the New Similarity Coefficient
216(3)
10.3.4 Illustrative Example
219(2)
10.4 Solution Procedure
221(4)
10.4.1 Stage 1
221(1)
10.4.2 Stage 2
222(3)
10.5 Comparative Study and Computational Performance
225(4)
10.5.1 Problem 1
226(1)
10.5.2 Problem 2
227(1)
10.5.3 Problem 3
228(1)
10.5.4 Computational Performance
229(1)
10.6 Conclusions
229(1)
References
230(3)
11 Fuzzy Approach to Quality Function Deployment-based Product Planning 233(18)
11.1 Introduction
233(2)
11.2 QFD-based Integration Model for New Product Development
235(2)
11.2.1 Relationship Between QFD Planning Process and Product Development Process
235(1)
11.2.2 QFD-based Integrated Product Development Process Model
235(2)
11.3 Problem Formulation of Product Planning
237(2)
11.4 Actual Achieved Degree and Planned Degree
239(1)
11.5 Formulation of Costs and Budget Constraint
239(2)
11.6 Maximizing Overall Customer Satisfaction Model
241(2)
11.7 Minimizing the Total Costs for Preferred Customer Satisfaction
243(1)
11.8 Genetic Algorithm-based Interactive Approach
244(3)
11.8.1 Formulation of Fuzzy Objective Function by Enterprise Satisfaction Level
244(1)
11.8.2 Transforming FP2 into a Crisp Model
245(1)
11.8.3 Genetic Algorithm-based Interactive Approach
246(1)
11.9 Illustrated Example and Simulation Results
247(2)
References
249(2)
12 Decision Making with Consideration of Association in Supply Chains 251(18)
12.1 Introduction
251(2)
12.2 Related Research
253(2)
12.2.1 ABC Classification
253(1)
12.2.2 Association Rule
253(1)
12.2.3 Evaluating Index
254(1)
12.3 Consideration and the Algorithm
255(6)
12.3.1 Expected Dollar Usage of Item(s)
255(1)
12.3.2 Further Analysis on EDU
256(2)
12.3.3 New Algorithm of Inventory Classification
258(1)
12.3.4 Enhanced Apriori Algorithm for Association Rules
258(2)
12.3.5 Other Considerations of Correlation
260(1)
12.4 Numerical Example and Discussion
261(2)
12.5 Empirical Study
263(4)
12.5.1 Datasets
263(1)
12.5.2 Experimental Results
263(4)
12.6 Concluding Remarks
267(1)
References
267(2)
13 Applying Self-organizing Maps to Master Data Making in Automatic Exterior Inspection 269(16)
13.1 Introduction
269(2)
13.2 Applying SOM to Make Master Data
271(5)
13.3 Experiments and Results
276(1)
13.4 The Evaluative Criteria of the Learning Effect
277(4)
13.4.1 Chi-squared Test
279(1)
13.4.2 Square Measure of Close Loops
279(1)
13.4.3 Distance Between Adjacent Neurons
280(1)
13.4.4 Monotony of Close Loops
280(1)
13.5 The Experimental Results of Comparing the Criteria
281(2)
13.6 Conclusions
283(1)
References
284(1)
14 Application for Privacy-preserving Data Mining 285(26)
14.1 Privacy-preserving Association Rule Mining
285(8)
14.1.1 Privacy-preserving Association Rule Mining in Centralized Data
285(2)
14.1.2 Privacy-preserving Association Rule Mining in Horizontal Partitioned Data
287(1)
14.1.3 Privacy-preserving Association Rule Mining in Vertically Partitioned Data
288(5)
14.2 Privacy-preserving Clustering
293(5)
14.2.1 Privacy-preserving Clustering in Centralized Data
293(1)
14.2.2 Privacy-preserving Clustering in Horizontal Partitioned Data
293(2)
14.2.3 Privacy-preserving Clustering in Vertically Partitioned Data
295(3)
14.3 A Scheme to Privacy-preserving Collaborative Data Mining
298(8)
14.3.1 Preliminaries
298(2)
14.3.2 The Analysis of the Previous Protocol
300(2)
14.3.3 A Scheme to Privacy-preserving Collaborative Data Mining
302(1)
14.3.4 Protocol Analysis
303(3)
14.4 Evaluation of Privacy Preservation
306(2)
14.5 Conclusion
308(1)
References
308(3)
Index 311
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.
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