| Contributing Authors | p. xix |
| Introduction | p. xxv |
| Knowledge Discovery | p. xxv |
| Exploratory Data Analysis and Data Mining | p. xxix |
| Traditional Methods | p. xxx |
| Self-Organking Maps | p. xxxiv |
| Simple Example: Mapping Scotch Whiskies | p. xxxvi |
| Overview | p. xi |
| Applications | |
| Let Financial Data Speak for Themselves | p. 3 |
| Initial Analysis of Financial Data | p. 3 |
| SOM as a Tool for Initial Data Analysis | p. 4 |
| Integrating SOM into a Decision-Support System | p. 18 |
| Projection of Long-term Interest Rates with Maps | p. 24 |
| Introduction | p. 24 |
| Building Blocks | p. 26 |
| Simulating Future Behavior using Historical Information | p. 28 |
| Application | p. 28 |
| Validation | p. 35 |
| Extension to Value-at-Risk | p. 36 |
| Conclusions | p. 38 |
| Picking Mutual Funds with Self-Organizing Maps | p. 39 |
| Exploratory Data Analysis | p. 39 |
| Morningstar Mutual Fund Database | p. 40 |
| A Simple Binary Example | p. 40 |
| Mapping Mutual Funds | p. 45 |
| Conclusions | p. 57 |
| Maps for Analyzing Failures of Small and Medium-sized Enterprises | p. 59 |
| Corporate Failure - Causes and Symptoms | p. 59 |
| Self-Organizing Map as a Tool for Financial Statement Analysis | p. 61 |
| The Data | p. 62 |
| Results | p. 63 |
| Summary | p. 71 |
| Self-Organizing Atlas of Russian Banks | p. 72 |
| Introduction | p. 72 |
| Overview of the Russian Banking System | p. 73 |
| Problem Formulation | p. 75 |
| Linear Analysis using PCA | p. 75 |
| Nonlinear Analysis or the Nonlinear PCA Extension | p. 76 |
| SOM of Russian Banks | p. 77 |
| A SOM Atlas of Russian Banks in 1994 | p. 79 |
| Evolution of Russian Banking from 1994 to 1995 | p. 81 |
| Conclusions | p. 81 |
| Investment Maps of Emerging Markets | p. 83 |
| Background | p. 83 |
| Performance and Risks of Investing in Emerging Markets | p. 84 |
| Patterns Among Emerging Markets | p. 86 |
| Strategic Implications of SOM for Investments in Emerging Markets | p. 101 |
| Conclusions | p. 105 |
| Color Plate Section follows Chapter 6 | |
| A Hybrid Neural Network System for Trading Financial Markets | p. 106 |
| Introduction | p. 106 |
| ISOG: Integrated Self-Organization and Genetics | p. 107 |
| Simulation Results | p. 109 |
| Conclusions | p. 116 |
| Real Estate Investment Appraisal of Land Properties using SOM | p. 117 |
| Introduction | p. 117 |
| Geographic Information Systems | p. 118 |
| Visualization | p. 119 |
| Scaling | p. 121 |
| Sensitivity Analysis | p. 122 |
| Portfolio Computation | p. 123 |
| Adaptation to New Observations | p. 124 |
| Other Examples | p. 124 |
| Conclusion | p. 127 |
| Real Estate Investment Appraisal of Buildings using SOM | p. 128 |
| Characteristic Features of the Finnish Real Estate Market | p. 128 |
| The Data | p. 129 |
| Preprocessing of the Data and the Research Method | p. 129 |
| The Results | p. 131 |
| Component Planes of the Map | p. 131 |
| Conclusions | p. 135 |
| Differential Patterns in Consumer Purchase Preferences using Self-Organizing Maps: A Case Study of China | p. 141 |
| Introduction | p. 141 |
| What do we Know about Chinese Consumers? | p. 142 |
| A Selective Review of the Prior Segmentation Research | p. 143 |
| The CEIBS Survey | p. 144 |
| Methodology | p. 144 |
| Major Results | p. 146 |
| Conclusions | p. 156 |
| Methodology, Tools and Techniques | |
| The SOM Methodology | p. 159 |
| Regression Principles | p. 159 |
| "Intelligent" Curve Fitting | p. 160 |
| The Self-Organizing Map Algorithm | p. 163 |
| The Neural Network Model of the SOM | p. 165 |
| Labeling the Neurons | p. 166 |
| The Batch Version of the SOM | p. 167 |
| Conclusion | p. 167 |
| Self-Organizing Maps of Large Document Collections | p. 168 |
| Introduction | p. 168 |
| WEBSOM for Document Map Applications | p. 169 |
| Document Map Creation | p. 175 |
| Conclusions | p. 178 |
| Software Tools for Self-Organizing Maps | p. 179 |
| Overview of Available Tools | p. 179 |
| SOM_PAK: The SOM Program Package | p. 181 |
| SOM: a MatLab Toolbox | p. 184 |
| Viscovery SOMine Lite: User-Friendly SOM at the Edge of Visualization | p. 187 |
| Appendix: Overview of Commercially Available Software Tools for Applying SOM | p. 191 |
| Tips for Processing and Color-coding of Self-Organizing Maps | p. 195 |
| The SOM Array | p. 195 |
| Scaling the Input Variables | p. 196 |
| Initialization of the Algorithm | p. 196 |
| Selection of the Neighborhood Function and Learning Rate | p. 196 |
| Automatic Color-Coding of Self-Organizing Maps | p. 197 |
| Best Practices in Data Mining using Self-Organizing Maps | p. 203 |
| Main Steps in using Self-Organizing Maps | p. 203 |
| Sample Application on Country Credit Risk Analysis | p. 212 |
| Conclusions | p. 229 |
| Notes | p. 230 |
| Glossary | p. 233 |
| Bibliography | p. 242 |
| Subject Index | p. 250 |
| Author Index | p. 255 |
| Website Index | p. 257 |
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