Data mining: concepts, methods and applications in management and engineering design

✍ Scribed by Yin, Yong;Kaku, Ikou;Tang, Jiafu;Zhu, Jianming

• Publisher: Springer
• Year: 2011
• Tongue: English
• Leaves: 319
• Series: Decision engineering
• Category: Library

✦ Synopsis

An essential text for readers wishing to use data mining methods to cope with management and engineering design problems within a company,Data Mining: Concepts, Methods and Applications in Management and Engineering Designstands out from other data mining books by introducing 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. Organized in two parts, the first part is a primer that introduces data mining to those readers who are not familiar with it. This section of the book discusses the methods that are commonly used in management and engineering design, including association rule mining, cluster analysis, grouping genetic algorithms, and fuzzy sets and fuzzy logic. The second part of the book focuses on applications in management and engineering design. This section covers almost all of the 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: Concepts, Methods and Applications in Management and Engineering Designpresents a number of state-of-the-art topics not covered in any other publication.

✦ Table of Contents

Preface......Page 5
Contents......Page 9
3.1 Introduction......Page 1
3.2.1 Definition of Fuzzy Sets......Page 3
3.2.3 Convexity and Concavity......Page 4
3.3.2 Interval Numbers......Page 6
3.3.4 Triangular Type Fuzzy Number......Page 7
3.3.5 Trapezoidal Fuzzy Numbers......Page 8
3.5.1 Classification of the Fuzzy Extreme Problems......Page 11
3.5.2 Classification of the Fuzzy Mathematical Programming Problems......Page 12
1.1 Decision Tree......Page 15
3.6 Brief Summary of Solution Methods for FOP......Page 16
3.6.1 Symmetric Approaches Based on Fuzzy Decision......Page 17
3.6.3 Asymmetric Approaches......Page 19
References......Page 22
2.1 Introduction......Page 23
2.2 Basic Concepts of Association Rule......Page 25
3.6.9 Fuzzy Genetic Algorithm......Page 26
References......Page 27
1.2 Cluster Analysis......Page 18
2.3.1 The Apriori Algorithm: Searching Frequent Itemsets......Page 28
2.3.2 Generating Association Rules from Frequent Itemsets......Page 30
2.4.1 Mining Multidimensional Association Rulesfrom Relational Databases......Page 31
2.4.2 Mining Association Rules with Time-window......Page 33
2.5 Summary......Page 36
References......Page 37
3.1 Introduction......Page 38
3.2.1 Definition of Fuzzy Sets......Page 40
3.2.2 Support and Cut Set......Page 41
3.3.1 Fuzzy Inequality with Tolerance......Page 42
3.3.2 Interval Numbers......Page 43
3.3.4 Triangular Type Fuzzy Number......Page 44
3.3.5 Trapezoidal Fuzzy Numbers......Page 45
3.4 Fuzzy Modeling and Fuzzy Optimization......Page 46
3.5.1 Classification of the Fuzzy Extreme Problems......Page 48
3.5.2 Classification of the Fuzzy Mathematical Programming Problems......Page 49
3.5.3 Classification of the Fuzzy Linear Programming Problems......Page 52
3.6 Brief Summary of Solution Methods for FOP......Page 53
3.6.1 Symmetric Approaches Based on Fuzzy Decision......Page 54
3.6.2 Symmetric Approach Based on Non-dominated Alternatives......Page 56
3.6.4 Possibility and Necessity Measure-based Approaches......Page 59
3.6.5 Asymmetric Approaches to PMP5 and PMP6......Page 60
3.6.6 Symmetric Approaches to the PMP7......Page 62
3.6.8 Generalized Approach by Angelov......Page 63
3.6.11 Penalty Function-based Approach......Page 64
4.1.1 Introduction......Page 68
4.1.2 Quadratic Programming Problems with Fuzzy Objective/Resource Constraints......Page 69
4.1.3 Fuzzy Optimal Solution and Best Balance Degree......Page 72
4.1.4 A Genetic Algorithm with Mutation Along the Weighted Gradient Direction......Page 73
4.1.5 Human–Computer Interactive Procedure......Page 75
4.1.6 A Numerical Illustration and Simulation Results......Page 77
4.2.1 Introduction......Page 79
4.2.2 Formulation of NLP Problems with Fuzzy Objective/Resource Constraints......Page 80
4.2.3 Inexact Approach Based on GA to Solve FO/RNP-1......Page 83
4.2.4 Overall Procedure for FO/RNP by Meansof Human–Computer Interaction......Page 85
4.2.5 Numerical Results and Analysis......Page 87
4.3.1 Introduction......Page 89
4.3.3 Fuzzy Feasible Domain and Fuzzy Optimal Solution Set......Page 92
4.3.4 Satisfying Solution and Crisp Optimal Solution......Page 93
4.3.5 General Scheme to Implement the FNLP-PC Model......Page 96
4.3.6 Numerical Illustration and Analysis......Page 97
4.4 Concluding Remarks......Page 98
References......Page 99
5.1 Introduction......Page 100
5.2 The Basic Concept of Self-organizing Map......Page 102
5.3 The Trial Discussion on Convergence of SOM......Page 105
5.4 Numerical Example......Page 109
References......Page 113
6.1 Introduction......Page 114
6.2.1 Security......Page 117
6.2.2 Privacy......Page 118
6.2.3 Data Mining......Page 120
6.3.1 The Characters of PPDM......Page 122
6.3.2 Classification of PPDM Techniques......Page 123
6.4 The Collusion Behaviors in PPDM......Page 127
References......Page 131
7.1 Introduction......Page 133
7.2 Literature Review......Page 135
7.3 The Model......Page 136
7.4 Comparative Statics......Page 139
References......Page 143
8.1 Introduction and Literature Review......Page 145
8.2 The Research Problem......Page 148
8.3.1 Two-function Products......Page 152
8.3.2 Three-function Products......Page 154
8.4.2 Three-function Products with Three Interfaces......Page 158
8.4.3 Implications......Page 163
8.5 A Summary of the Model......Page 164
8.6 Conclusion......Page 166
9.1 Introduction......Page 168
9.2.1 Machine-part Cell Formation......Page 171
9.2.2 Similarity Coefficient Methods (SCM)......Page 172
9.3.2 Objective of this Study......Page 173
9.3.3 Why SCM Are More Flexible......Page 174
9.4 Taxonomy for Similarity Coefficients Employed in Cellular Manufacturing......Page 176
9.5 Mapping SCM Studies onto the Taxonomy......Page 180
9.6.1 Production Information-based Similarity Coefficients......Page 187
9.6.2 Historical Evolution of Similarity Coefficients......Page 190
9.7.1 Objective......Page 191
9.7.2 Previous Comparative Studies......Page 192
9.8.1 Tested Similarity Coefficients......Page 193
9.8.2 Datasets......Page 194
9.8.3 Clustering Procedure......Page 198
9.8.4 Performance Measures......Page 199
9.9 Comparison and Results......Page 202
9.10 Conclusions......Page 208
References......Page 209
10.1 Introduction......Page 217
10.2.1 Background......Page 219
10.2.2 Objective of this Study and Drawbacksof Previous Research......Page 221
10.3.1 Nomenclature......Page 223
10.3.2 Generalized Similarity Coefficient......Page 225
10.3.3 Definition of the New Similarity Coefficient......Page 226
10.3.4 Illustrative Example......Page 229
10.4.1 Stage 1......Page 231
10.4.2 Stage 2......Page 232
10.5 Comparative Study and Computational Performance......Page 235
10.5.1 Problem 1......Page 236
10.5.2 Problem 2......Page 237
10.5.3 Problem 3......Page 238
10.5.4 Computational Performance......Page 239
References......Page 240
11.1 Introduction......Page 242
11.2.1 Relationship Between QFD Planning Process and Product Development Process......Page 244
11.3 Problem Formulation of Product Planning......Page 246
11.5 Formulation of Costs and Budget Constraint......Page 248
11.6 Maximizing Overall Customer Satisfaction Model......Page 250
11.7 Minimizing the Total Costs for Preferred Customer Satisfaction......Page 252
11.8.1 Formulation of Fuzzy Objective Function by Enterprise Satisfaction Level......Page 253
11.8.2 Transforming FP2 into a Crisp Model......Page 254
11.8.3 Genetic Algorithm-based Interactive Approach......Page 255
11.9 Illustrated Example and Simulation Results......Page 256
References......Page 258
12.1 Introduction......Page 259
12.2.2 Association Rule......Page 261
12.2.3 Evaluating Index......Page 262
12.3.1 Expected Dollar Usage of Item(s)......Page 263
12.3.2 Further Analysis on EDU......Page 264
12.3.4 Enhanced Apriori Algorithm for Association Rules......Page 266
12.3.5 Other Considerations of Correlation......Page 268
12.4 Numerical Example and Discussion......Page 269
12.5.1 Datasets......Page 271
12.6 Concluding Remarks......Page 275
13.1 Introduction......Page 277
13.2 Applying SOM to Make Master Data......Page 279
13.3 Experiments and Results......Page 284
13.4 The Evaluative Criteria of the Learning Effect......Page 285
13.4.1 Chi-squared Test......Page 287
13.4.3 Distance Between Adjacent Neurons......Page 288
13.5 The Experimental Results of Comparing the Criteria......Page 289
13.6 Conclusions......Page 291
References......Page 292
14.1.1 Privacy-preserving Association Rule Miningin Centralized Data......Page 293
14.1.2 Privacy-preserving Association Rule Mining in Horizontal Partitioned Data......Page 295
14.1.3 Privacy-preserving Association Rule Mining in Vertically Partitioned Data......Page 296
14.2.1 Privacy-preserving Clustering in Centralized Data......Page 301
14.2.3 Privacy-preserving Clustering in Vertically Partitioned Data......Page 303
14.3.1 Preliminaries......Page 306
14.3.2 The Analysis of the Previous Protocol......Page 308
14.3.3 A Scheme to Privacy-preserving Collaborative Data Mining......Page 310
14.3.4 Protocol Analysis......Page 311
14.4 Evaluation of Privacy Preservation......Page 314
14.5 Conclusion......Page 316
Index......Page 318