Yin / Kaku / Tang Data Mining

Concepts, Methods and Applications in Management and Engineering Design
1. Auflage 2011
ISBN: 978-1-84996-338-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 312 Seiten

Reihe: Decision Engineering

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.

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.

Autoren/Hrsg.

Yin, Yong

Kaku, Ikou

Tang, Jiafu

Zhu, Jianming

Inhaltsverzeichnis

1;Preface;6
2;Contents;10
3;1 Decision Analysis and Cluster Analysis;16
3.1;1.1 Decision Tree;16
3.2;1.2 Cluster Analysis;19
3.3;References;23
4;2 Association Rules Mining in Inventory Database ;24
4.1;2.1 Introduction;24
4.2;2.2 Basic Concepts of Association Rule;26
4.3;2.3 Mining Association Rules;29
4.3.1;2.3.1 The Apriori Algorithm: Searching Frequent Itemsets;29
4.3.2;2.3.2 Generating Association Rules from Frequent Itemsets;31
4.4;2.4 Related Studies on Mining Association Rulesin Inventory Database;32
4.4.1;2.4.1 Mining Multidimensional Association Rulesfrom Relational Databases;32
4.4.2;2.4.2 Mining Association Rules with Time-window;34
4.5;2.5 Summary;37
4.6;References;38
5;3 Fuzzy Modeling and Optimization: Theory and Methods;39
5.1;3.1 Introduction;39
5.2;3.2 Basic Terminology and Definition;41
5.2.1;3.2.1 Definition of Fuzzy Sets;41
5.2.2;3.2.2 Support and Cut Set;42
5.2.3;3.2.3 Convexity and Concavity;42
5.3;3.3 Operations and Properties for Generally Used Fuzzy Numbers;43
5.3.1;3.3.1 Fuzzy Inequality with Tolerance;43
5.3.2;3.3.2 Interval Numbers;44
5.3.3;3.3.3 L–R Type Fuzzy Number;45
5.3.4;3.3.4 Triangular Type Fuzzy Number;45
5.3.5;3.3.5 Trapezoidal Fuzzy Numbers;46
5.4;3.4 Fuzzy Modeling and Fuzzy Optimization;47
5.5;3.5 Classification of a Fuzzy Optimization Problem;49
5.5.1;3.5.1 Classification of the Fuzzy Extreme Problems;49
5.5.2;3.5.2 Classification of the Fuzzy Mathematical Programming Problems;50
5.5.3;3.5.3 Classification of the Fuzzy Linear Programming Problems;53
5.6;3.6 Brief Summary of Solution Methods for FOP;54
5.6.1;3.6.1 Symmetric Approaches Based on Fuzzy Decision;55
5.6.2;3.6.2 Symmetric Approach Based on Non-dominated Alternatives;57
5.6.3;3.6.3 Asymmetric Approaches;57
5.6.4;3.6.4 Possibility and Necessity Measure-based Approaches;60
5.6.5;3.6.5 Asymmetric Approaches to PMP5 and PMP6;61
5.6.6;3.6.6 Symmetric Approaches to the PMP7;63
5.6.7;3.6.7 Interactive Satisfying Solution Approach;63
5.6.8;3.6.8 Generalized Approach by Angelov;64
5.6.9;3.6.9 Fuzzy Genetic Algorithm;64
5.6.10;3.6.10 Genetic-based Fuzzy Optimal Solution Method;65
5.6.11;3.6.11 Penalty Function-based Approach;65
5.7;References;65
6;4 Genetic Algorithm-based Fuzzy Nonlinear Programming;69
6.1;4.1 GA-based Interactive Approach for QP Problemswith Fuzzy Objective and Resources;69
6.1.1;4.1.1 Introduction;69
6.1.2;4.1.2 Quadratic Programming Problems with Fuzzy Objective/Resource Constraints;70
6.1.3;4.1.3 Fuzzy Optimal Solution and Best Balance Degree;73
6.1.4;4.1.4 A Genetic Algorithm with Mutation Along the Weighted Gradient Direction;74
6.1.5;4.1.5 Human–Computer Interactive Procedure;76
6.1.6;4.1.6 A Numerical Illustration and Simulation Results;78
6.2;4.2 Nonlinear Programming Problems with Fuzzy Objectiveand Resources;80
6.2.1;4.2.1 Introduction;80
6.2.2;4.2.2 Formulation of NLP Problems with Fuzzy Objective/Resource Constraints;81
6.2.3;4.2.3 Inexact Approach Based on GA to Solve FO/RNP-1;84
6.2.4;4.2.4 Overall Procedure for FO/RNP by Meansof Human–Computer Interaction;86
6.2.5;4.2.5 Numerical Results and Analysis ;88
6.3;4.3 A Non-symmetric Model for Fuzzy NLP Problemswith Penalty Coefficients;90
6.3.1;4.3.1 Introduction ;90
6.3.2;4.3.2 Formulation of Fuzzy Nonlinear Programming Problems with Penalty Coefficients;90
6.3.3;4.3.3 Fuzzy Feasible Domain and Fuzzy Optimal Solution Set;93
6.3.4;4.3.4 Satisfying Solution and Crisp Optimal Solution;94
6.3.5;4.3.5 General Scheme to Implement the FNLP-PC Model;97
6.3.6;4.3.6 Numerical Illustration and Analysis;98
6.4;4.4 Concluding Remarks;99
6.5;References;100
7;5 Neural Network and Self-organizing Maps;101
7.1;5.1 Introduction;101
7.2;5.2 The Basic Concept of Self-organizing Map;103
7.3;5.3 The Trial Discussion on Convergence of SOM;106
7.4;5.4 Numerical Example;110
7.5;5.5 Conclusion;114
7.6;References;114
8;6 Privacy-preserving Data Mining;115
8.1;6.1 Introduction;115
8.2;6.2 Security, Privacy and Data Mining;118
8.2.1;6.2.1 Security;118
8.2.2;6.2.2 Privacy;119
8.2.3;6.2.3 Data Mining;121
8.3;6.3 Foundation of PPDM;123
8.3.1;6.3.1 The Characters of PPDM;123
8.3.2;6.3.2 Classification of PPDM Techniques;124
8.4;6.4 The Collusion Behaviors in PPDM;128
8.5;6.5 Summary;132
8.6;References;132
9;7 Supply Chain Design Using Decision Analysis;134
9.1;7.1 Introduction;134
9.2;7.2 Literature Review;136
9.3;7.3 The Model;137
9.4;7.4 Comparative Statics;140
9.5;7.5 Conclusion;144
9.6;References;144
10;8 Product Architecture and Product Development Processfor Global Performance;146
10.1;8.1 Introduction and Literature Review;146
10.2;8.2 The Research Problem;149
10.3;8.3 The Models;153
10.3.1;8.3.1 Two-function Products;153
10.3.2;8.3.2 Three-function Products;155
10.4;8.4 Comparisons and Implications;159
10.4.1;8.4.1 Three-function Products with Two Interfaces;159
10.4.2;8.4.2 Three-function Products with Three Interfaces;159
10.4.3;8.4.3 Implications;164
10.5;8.5 A Summary of the Model;165
10.6;8.6 Conclusion;167
10.7;References;167
11;9 Application of Cluster Analysis to Cellular Manufacturing;169
11.1;9.1 Introduction;169
11.2;9.2 Background;172
11.2.1;9.2.1 Machine-part Cell Formation;172
11.2.2;9.2.2 Similarity Coefficient Methods (SCM);173
11.3;9.3 Why Present a Taxonomy on Similarity Coefficients?;173
11.3.1;9.3.1 Past Review Studies on SCM;174
11.3.2;9.3.2 Objective of this Study;174
11.3.3;9.3.3 Why SCM Are More Flexible;175
11.4;9.4 Taxonomy for Similarity Coefficients Employed in Cellular Manufacturing;177
11.5;9.5 Mapping SCM Studies onto the Taxonomy;181
11.6;9.6 General Discussion;188
11.6.1;9.6.1 Production Information-based Similarity Coefficients;188
11.6.2;9.6.2 Historical Evolution of Similarity Coefficients;191
11.7;9.7 Comparative Study of Similarity Coefficients;192
11.7.1;9.7.1 Objective;192
11.7.2;9.7.2 Previous Comparative Studies;193
11.8;9.8 Experimental Design;194
11.8.1;9.8.1 Tested Similarity Coefficients;194
11.8.2;9.8.2 Datasets;195
11.8.3;9.8.3 Clustering Procedure;199
11.8.4;9.8.4 Performance Measures;200
11.9;9.9 Comparison and Results;203
11.10;9.10 Conclusions;209
11.11;References;210
12;10 Manufacturing Cells Design by Cluster Analysis;218
12.1;10.1 Introduction;218
12.2;10.2 Background, Difficulty and Objective of this Study;220
12.2.1;10.2.1 Background;220
12.2.2;10.2.2 Objective of this Study and Drawbacksof Previous Research;222
12.3;10.3 Problem Formulation;224
12.3.1;10.3.1 Nomenclature;224
12.3.2;10.3.2 Generalized Similarity Coefficient;226
12.3.3;10.3.3 Definition of the New Similarity Coefficient;227
12.3.4;10.3.4 Illustrative Example;230
12.4;10.4 Solution Procedure;232
12.4.1;10.4.1 Stage 1;232
12.4.2;10.4.2 Stage 2;233
12.5;10.5 Comparative Study and Computational Performance;236
12.5.1;10.5.1 Problem 1;237
12.5.2;10.5.2 Problem 2;238
12.5.3;10.5.3 Problem 3;239
12.5.4;10.5.4 Computational Performance;240
12.6;10.6 Conclusions;240
12.7;References;241
13;11 Fuzzy Approach to Quality Function Deployment-based Product Planning;243
13.1;11.1 Introduction;243
13.2;11.2 QFD-based Integration Model for New Product Development;245
13.2.1;11.2.1 Relationship Between QFD Planning Process and Product Development Process;245
13.2.2;11.2.2 QFD-based Integrated Product Development ProcessModel;245
13.3;11.3 Problem Formulation of Product Planning;247
13.4;11.4 Actual Achieved Degree and Planned Degree ;249
13.5;11.5 Formulation of Costs and Budget Constraint;249
13.6;11.6 Maximizing Overall Customer Satisfaction Model;251
13.7;11.7 Minimizing the Total Costs for Preferred Customer Satisfaction;253
13.8;11.8 Genetic Algorithm-based Interactive Approach;254
13.8.1;11.8.1 Formulation of Fuzzy Objective Function by Enterprise Satisfaction Level;254
13.8.2;11.8.2 Transforming FP2 into a Crisp Model;255
13.8.3;11.8.3 Genetic Algorithm-based Interactive Approach;256
13.9;11.9 Illustrated Example and Simulation Results;257
13.10;References;259
14;12 Decision Making with Consideration of Associationin Supply Chains;260
14.1;12.1 Introduction;260
14.2;12.2 Related Research;262
14.2.1;12.2.1 ABC Classification;262
14.2.2;12.2.2 Association Rule;262
14.2.3;12.2.3 Evaluating Index;263
14.3;12.3 Consideration and the Algorithm;264
14.3.1;12.3.1 Expected Dollar Usage of Item(s);264
14.3.2;12.3.2 Further Analysis on EDU;265
14.3.3;12.3.3 New Algorithm of Inventory Classification;267
14.3.4;12.3.4 Enhanced Apriori Algorithm for Association Rules;267
14.3.5;12.3.5 Other Considerations of Correlation;269
14.4;12.4 Numerical Example and Discussion;270
14.5;12.5 Empirical Study;272
14.5.1;12.5.1 Datasets;272
14.5.2;12.5.2 Experimental Results;272
14.6;12.6 Concluding Remarks;276
14.7;References;276
15;13 Applying Self-organizing Maps to Master Data Makingin Automatic Exterior Inspection;278
15.1;13.1 Introduction;278
15.2;13.2 Applying SOM to Make Master Data;280
15.3;13.3 Experiments and Results;285
15.4;13.4 The Evaluative Criteria of the Learning Effect;286
15.4.1;13.4.1 Chi-squared Test;288
15.4.2;13.4.2 Square Measure of Close Loops;288
15.4.3;13.4.3 Distance Between Adjacent Neurons;289
15.4.4;13.4.4 Monotony of Close Loops;289
15.5;13.5 The Experimental Results of Comparing the Criteria;290
15.6;13.6 Conclusions;292
15.7;References;293
16;14 Application for Privacy-preserving Data Mining;294
16.1;14.1 Privacy-preserving Association Rule Mining;294
16.1.1;14.1.1 Privacy-preserving Association Rule Miningin Centralized Data;294
16.1.2;14.1.2 Privacy-preserving Association Rule Mining in Horizontal Partitioned Data;296
16.1.3;14.1.3 Privacy-preserving Association Rule Mining in Vertically Partitioned Data;297
16.2;14.2 Privacy-preserving Clustering;302
16.2.1;14.2.1 Privacy-preserving Clustering in Centralized Data;302
16.2.2;14.2.2 Privacy-preserving Clusteringin Horizontal Partitioned Data;302
16.2.3;14.2.3 Privacy-preserving Clustering in Vertically Partitioned Data;304
16.3;14.3 A Scheme to Privacy-preserving Collaborative Data Mining;307
16.3.1;14.3.1 Preliminaries;307
16.3.2;14.3.2 The Analysis of the Previous Protocol;309
16.3.3;14.3.3 A Scheme to Privacy-preserving Collaborative Data Mining;311
16.3.4;14.3.4 Protocol Analysis;312
16.4;14.4 Evaluation of Privacy Preservation;315
16.5;14.5 Conclusion;317
16.6;References;317
17;Index;319