Mining of data with complex structures

Mining of Data with Complex Structures: - Clarifies the type and nature of data with complex structure including sequences, trees and graphs - Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining. - Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints. - Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.) - Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees. - Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees. - Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach. - Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies. - Details the extension of the TMG framework for sequence mining - Provides an overview of the future research direction with respect to technical extensions and application areas The primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains Cover......Page 1 Front Matter......Page 2 Classifications of Benchmarks......Page 3 Truss Design Problems......Page 4 Genetic Algorithm......Page 7 Immune Algorithm......Page 8 Particle Swarm Optimization......Page 9 Non-truss Design Problems......Page 10 Data Transformation......Page 5 Data Reduction......Page 6 Simulation-Based Tuning and Tuning Space Mapping......Page 11 The Proposed Enhanced SVM Model......Page 12 Data Mining and Knowledge Extraction......Page 14 Conclusion......Page 15 Computational Optimization......Page 17 A Numerical Example and Empirical Results......Page 13 Optimization Procedure......Page 18 Discussions and Further Research......Page 20 References......Page 21 Numerical Solvers......Page 23 Simulation Efficiency......Page 24 Introduction......Page 16 Optimization Algorithms......Page 19 Choice of Algorithms......Page 22 Conclusions and Final Remarks......Page 26 Introduction......Page 27 Latest Developments......Page 25 Newton's Method and Hill-Climbing......Page 28 Conjugate Gradient Method......Page 29 Pattern Search......Page 30 Trust-Region Method......Page 31 Simulated Annealling......Page 32 Genetic Algorithms and Differential Evolution......Page 33 Particle Swarm Optimization......Page 35 Harmony Search......Page 36 Firefly Algorithm......Page 37 Cuckoo Search......Page 38 Characteristics of Metaheuristics......Page 40 Generalized Evolutionary Walk Algorithm (GEWA)......Page 41 To Be Inspired or Not to Be Inspired......Page 43 Surrogate-Based Methods*......Page 46 Introduction......Page 47 Surrogate-Based Optimization......Page 48 Surrogate Models......Page 50 Design of Experiments......Page 52 Surrogate Modeling Techniques......Page 54 Model Validation......Page 58 Surrogate-Based Optimization Techniques......Page 62 Approximation Model Management Optimization......Page 63 Manifold Mapping......Page 65 Surrogate Management Framework......Page 66 Exploitation versus Exploration......Page 68 References......Page 69 Introduction......Page 73 Derivative-Free Optimization......Page 75 Pattern Search Methods......Page 77 Derivative-Free Optimization with Interpolation and Approximation Models......Page 79 Evolutionary Algorithms......Page 80 Estimation of Distribution Algorithms......Page 84 Differential Evolution......Page 85 Penalty Functions......Page 86 Augmented Lagrangian Method......Page 87 Filter Method......Page 88 Other Approaches......Page 89 Concluding Remarks......Page 90 References......Page 91 Introduction......Page 96 Copula Model......Page 97 The CRT Method......Page 101 Optimization Technique......Page 102 Application......Page 105 Concluding Remarks......Page 109 References......Page 110 Introduction......Page 112 Related Work......Page 113 Simulation Optimization......Page 114 Features of a General Model......Page 116 Features of the Simulation Model......Page 118 Particle Swarm Optimization......Page 122 System Setup......Page 123 Results and Discussion......Page 126 Conclusion and Future Work......Page 132 References......Page 133 Traditional and Hybrid Derivative-Free Optimization Approaches for Black Box Functions......Page 136 Introduction and Motivation......Page 137 A Motivating Example......Page 138 Genetic Algorithms (GAs)......Page 141 Deterministic Sampling Methods......Page 143 Statistical Emulation......Page 149 Some DFO Hybrids......Page 150 APPS-TGP......Page 151 EAGLS......Page 153 DIRECT-IFFCO......Page 155 Summary and Conclusion......Page 156 References......Page 157 Introduction......Page 163 Direct Approaches......Page 164 Surrogate-Based Design Optimization......Page 166 Surrogate Models for Microwave Engineering......Page 168 Microwave Simulation-Driven Design Exploiting Physically-Based Surrogates......Page 171 Space Mapping......Page 172 Simulation-Based Tuning and Tuning Space Mapping......Page 173 Shape-Preserving Response Prediction......Page 177 Multi-fidelity Optimization Using Coarse-Discretization EM Models......Page 180 Optimization Using Adaptively Adjusted Design Specifications......Page 182 References......Page 185 Introduction......Page 189 Problem Formulation......Page 192 Governing Equations......Page 195 Numerical Modeling......Page 198 Direct Optimization......Page 205 Gradient-Based Methods......Page 206 The Concept......Page 207 Surrogate Modeling......Page 208 Optimization Techniques......Page 210 Summary......Page 214 References......Page 215 Evolutionary Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization......Page 221 Introduction......Page 222 Basic Concepts......Page 223 Pareto Optimality......Page 224 Pareto Front......Page 225 Surrogate-Based Optimization......Page 226 Hybrid MOEA Optimization......Page 229 Robust Design Optimization......Page 230 Multi-Disciplinary Design Optimization......Page 232 Data Mining and Knowledge Extraction......Page 234 Objective Functions......Page 236 Evolutionary Algorithm......Page 239 Results......Page 243 Conclusions and Final Remarks......Page 246 References......Page 247 Basic Concept of Classification and Support Vector Machines......Page 251 Data Transformation......Page 255 Data Reduction......Page 256 Genetic Algorithm......Page 257 Immune Algorithm......Page 258 Particle Swarm Optimization......Page 259 Rule Extraction Form Support Vector Machines......Page 260 The Proposed Enhanced SVM Model......Page 262 A Numerical Example and Empirical Results......Page 263 Conclusion......Page 265 References......Page 266 Introduction to Benchmark Structural Design......Page 269 Structural Engineering Design and Optimization......Page 270 Classifications of Benchmarks......Page 271 Truss Design Problems......Page 272 Non-truss Design Problems......Page 278 Discussions and Further Research......Page 288 References......Page 289 Back Matter......Page 292 Annotation Mining of Data with Complex Structures:- Clarifies the type and nature of data with complex structure including sequences, trees and graphs- Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining.-Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints.- Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.)- Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees.- Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees.- Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach.- Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies.- Details the extension of the TMG framework for sequence mining- Provides an overview of the future research direction with respect to technical extensions and application areasThe primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains Mining of Data with Complex Structures explores nature of data with complex structure including sequences, trees and graphs. Readers will find a detailed description of the state-of-the-art of sequence mining, tree mining and graph mining, and more.