Mining of data with complex structures

✍ Scribed by Hadzic, Fedja;Tan, Henry;Dillon, Tharam S

• Publisher: Springer-Verlag
• Year: 2011
• Tongue: English
• Leaves: 292
• Series: Studies in computational intelligence 333
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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