
Data Mining
Concepts, Methods and Applications in Management and Engineering Design
By:Â Yong Yin, Ikou Kaku, Jiafu Tang
Hardcover | 7 January 2011
At a Glance
328 Pages
23.5 x 15.88 x 1.91
Hardcover
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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 Design stands 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 Design presents a number of state-of-the-art topics not covered in any other publication.
Industry Reviews
| 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 |
| Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9781849963374
ISBN-10: 1849963371
Series: Decision Engineering
Published: 7th January 2011
Format: Hardcover
Language: English
Number of Pages: 328
Audience: Professional and Scholarly
Publisher: Springer Nature B.V.
Country of Publication: GB
Dimensions (cm): 23.5 x 15.88 x 1.91
Weight (kg): 0.6
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- Non-FictionEngineering & TechnologyTechnology in GeneralTechnical Design
- Non-FictionComputing & I.T.DatabasesData Mining
- Non-FictionBusiness & ManagementBusiness Mathematics & Systems
- Non-FictionEngineering & TechnologyTechnology in GeneralEngineering in General
- Non-FictionBusiness & ManagementOperational Research
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