Data Mining

by Yin, Yong; Kaku, Ikou; Tang, Jiafu; Zhu, JianMing

• ISBN13: 9781849963374
• ISBN10: 1849963371
• Format: Hardcover
• Copyright: 2011-01-06
• Publisher: Springer-Verlag New York Inc

Summary

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.

Table of Contents

Decision Analysis and Cluster Analysis	p. 1
Decision Tree	p. 1
Cluster Analysis	p. 4
References	p. 8
Association Rules Mining in Inventory Database	p. 9
Introduction	p. 9
Basic Concepts of Association Rule	p. 11
Mining Association Rules	p. 14
The Apriori Algorithm: Searching Frequent Itemsets	p. 14
Generating Association Rules from Frequent Itemsets	p. 16
Related Studies on Mining Association Rules in Inventory Database	p. 17
Mining Multidimensional Association Rules from Relational Databases	p. 17
Mining Association Rules with Time-window	p. 19
Summary	p. 22
References	p. 23
Fuzzy Modeling and Optimization: Theory and Methods	p. 25
Introduction	p. 25
Basic Terminology and Definition	p. 27
Definition of Fuzzy Sets	p. 27
Support and Cut Set	p. 28
Convexity and Concavity	p. 28
Operations and Properties for Generally Used Fuzzy Numbers	p. 29
Fuzzy Inequality with Tolerance	p. 29
Interval Numbers	p. 30
L-R Type Fuzzy Number	p. 31
Triangular Type Fuzzy Number	p. 31
Trapezoidal Fuzzy Numbers	p. 32
Fuzzy Modeling and Fuzzy Optimization	p. 33
Classification of a Fuzzy Optimization Problem	p. 35
Classification of the Fuzzy Extreme Problems	p. 35
Classification of the Fuzzy Mathematical Programming Problems	p. 36
Classification of the Fuzzy Linear Programming Problems	p. 39
Brief Summary of Solution Methods for FOP	p. 40
Symmetric Approaches Based on Fuzzy Decision	p. 41
Symmetric Approach Based on Non-dominated Alternatives	p. 43
Asymmetric Approaches	p. 43
Possibility and Necessity Measure-based Approaches	p. 46
Asymmetric Approaches to PMP5 and PMP6	p. 47
Symmetric Approaches to the PMP7	p. 49
Interactive Satisfying Solution Approach	p. 49
Generalized Approach by Angelov	p. 50
Fuzzy Genetic Algorithm	p. 50
Genetic-based Fuzzy Optimal Solution Method	p. 51
Penalty Function-based Approach	p. 51
References	p. 51
Genetic Algorithm-based Fuzzy Nonlinear Programming	p. 55
GA-based Interactive Approach for QP Problems with Fuzzy Objective and Resources	p. 55
Introduction	p. 55
Quadratic Programming Problems with Fuzzy Objective/Resource Constraints	p. 56
Fuzzy Optimal Solution and Best Balance Degree	p. 59
A Genetic Algorithm with Mutation Along the Weighted Gradient Direction	p. 60
Human-Computer Interactive Procedure	p. 62
A Numerical Illustration and Simulation Results	p. 64
Nonlinear Programming Problems with Fuzzy Objective and Resources	p. 66
Introduction	p. 66
Formulation of NLP Problems with Fuzzy Objective/Resource Constraints	p. 67
Inexact Approach Based on GA to Solve FO/RNP-1	p. 70
Overall Procedure for FO/RNP by Means of Human-Computer Interaction	p. 72
Numerical Results and Analysis	p. 74
A Non-symmetric Model for Fuzzy NLP Problems with Penalty Coefficients	p. 76
Introduction	p. 76
Formulation of Fuzzy Nonlinear Programming Problems with Penalty Coefficients	p. 76
Fuzzy Feasible Domain and Fuzzy Optimal Solution Set	p. 79
Satisfying Solution and Crisp Optimal Solution	p. 80
General Scheme to Implement the FNLP-PC Model	p. 83
Numerical Illustration and Analysis	p. 84
Concluding Remarks	p. 85
References	p. 86
Neural Network and Self-organizing Maps	p. 87
Introduction	p. 87
The Basic Concept of Self-organizing Map	p. 89
The Trial Discussion on Convergence of SOM	p. 92
Numerical Example	p. 96
Conclusion	p. 100
References	p. 100
Privacy-preserving Data Mining	p. 101
Introduction	p. 101
Security, Privacy and Data Mining	p. 104
Security	p. 104
Privacy	p. 105
Data Mining	p. 107
Foundation of PPDM	p. 109
The Characters of PPDM	p. 109
Classification of PPDM Techniques	p. 110
The Collusion Behaviors in PPDM	p. 114
Summary	p. 118
References	p. 118
Supply Chain Design Using Decision Analysis	p. 121
Introduction	p. 121
Literature Review	p. 123
The Model	p. 124
Comparative Statics	p. 127
Conclusion	p. 131
References	p. 131
Product Architecture and Product Development Process for Global Performance	p. 133
Introduction and Literature Review	p. 133
The Research Problem	p. 136
The Models	p. 140
Two-function Products	p. 140
Three-function Products	p. 142
Comparisons and Implications	p. 146
Three-function Products with Two Interfaces	p. 146
Three-function Products with Three Interfaces	p. 146
Implications	p. 151
A Summary of the Model	p. 152
Conclusion	p. 154
References	p. 154
Application of Cluster Analysis to Cellular Manufacturing	p. 157
Introduction	p. 157
Background	p. 160
Machine-part Cell Formation	p. 160
Similarity Coefficient Methods (SCM)	p. 161
Why Present a Taxonomy on Similarity Coefficients?	p. 161
Past Review Studies on SCM	p. 162
Objective of this Study	p. 162
Why SCM Are More Flexible	p. 163
Taxonomy for Similarity Coefficients Employed in Cellular Manufacturing	p. 165
Mapping SCM Studies onto the Taxonomy	p. 169
General Discussion	p. 176
Production Information-based Similarity Coefficients	p. 176
Historical Evolution of Similarity Coefficients	p. 179
Comparative Study of Similarity Coefficients	p. 180
Objective	p. 180
Previous Comparative Studies	p. 181
Experimental Design	p. 182
Tested Similarity Coefficients	p. 182
Datasets	p. 183
Clustering Procedure	p. 187
Performance Measures	p. 188
Comparison and Results	p. 191
Conclusions	p. 197
References	p. 198
Manufacturing Cells Design by Cluster Analysis	p. 207
Introduction	p. 207
Background, Difficulty and Objective of this Study	p. 209
Background	p. 209
Objective of this Study and Drawbacks of Previous Research	p. 211
Problem Formulation	p. 213
Nomenclature	p. 213
Generalized Similarity Coefficient	p. 215
Definition of the New Similarity Coefficient	p. 216
Illustrative Example	p. 219
Solution Procedure	p. 221
Stage 1	p. 221
Stage 2	p. 222
Comparative Study and Computational Performance	p. 225
Problem 1	p. 226
Problem 2	p. 227
Problem 3	p. 228
Computational Performance	p. 229
Conclusions	p. 229
References	p. 230
Fuzzy Approach to Quality Function Deployment-based Product Planning	p. 233
Introduction	p. 233
QFD-based Integration Model for New Product Development	p. 235
Relationship Between QFD Planning Process and Product Development Process	p. 235
QFD-based Integrated Product Development Process Model	p. 235
Problem Formulation of Product Planning	p. 237
Actual Achieved Degree and Planned Degree	p. 239
Formulation of Costs and Budget Constraint	p. 239
Maximizing Overall Customer Satisfaction Model	p. 241
Minimizing the Total Costs for Preferred Customer Satisfaction	p. 243
Genetic Algorithm-based Interactive Approach	p. 244
Formulation of Fuzzy Objective Function by Enterprise Satisfaction Level	p. 244
Transforming FP2 into a Crisp Model	p. 245
Genetic Algorithm-based Interactive Approach	p. 246
Illustrated Example and Simulation Results	p. 247
References	p. 249
Decision Making with Consideration of Association in Supply Chains	p. 251
Introduction	p. 251
Related Research	p. 253
ABC Classification	p. 253
Association Rule	p. 253
Evaluating Index	p. 254
Consideration and the Algorithm	p. 255
Expected Dollar Usage of Item(s)	p. 255
Further Analysis on EDU	p. 256
New Algorithm of Inventory Classification	p. 258
Enhanced Apriori Algorithm for Association Rules	p. 258
Other Considerations of Correlation	p. 260
Numerical Example and Discussion	p. 261
Empirical Study	p. 263
Datasets	p. 263
Experimental Results	p. 263
Concluding Remarks	p. 267
References	p. 267
Applying Self-organizing Maps to Master Data Making in Automatic Exterior Inspection	p. 269
Introduction	p. 269
Applying SOM to Make Master Data	p. 271
Experiments and Results	p. 276
The Evaluative Criteria of the Learning Effect	p. 277
Chi-squared Test	p. 279
Square Measure of Close Loops	p. 279
Distance Between Adjacent Neurons	p. 280
Monotony of Close Loops	p. 280
The Experimental Results of Comparing the Criteria	p. 281
Conclusions	p. 283
References	p. 284
Application for Privacy-preserving Data Mining	p. 285
Privacy-preserving Association Rule Mining	p. 285
Privacy-preserving Association Rule Mining in Centralized Data	p. 285
Privacy-preserving Association Rule Mining in Horizontal Partitioned Data	p. 287
Privacy-preserving Association Rule Mining in Vertically Partitioned Data	p. 288
Privacy-preserving Clustering	p. 293
Privacy-preserving Clustering in Centralized Data	p. 293
Privacy-preserving Clustering in Horizontal Partitioned Data	p. 293
Privacy-preserving Clustering in Vertically Partitioned Data	p. 295
A Scheme to Privacy-preserving Collaborative Data Mining	p. 298
Preliminaries	p. 298
The Analysis of the Previous Protocol	p. 300
A Scheme to Privacy-preserving Collaborative Data Mining	p. 302
Protocol Analysis	p. 303
Evaluation of Privacy Preservation	p. 306
Conclusion	p. 308
References	p. 308
Index	p. 311
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