| Contents | 6 |
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| Preface | 7 |
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| 1. Trends in Data Mining and Knowledge Discovery | 13 |
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| 1.1 Knowledge Discovery and Data Mining Process | 13 |
| 1.2 Six-Step Knowledge Discovery and Data Mining Process | 17 |
| 1.3 New Technologies | 22 |
| 1.4 Future of Data Mining and Knowledge Discovery? | 28 |
| 1.5 Conclusions | 32 |
| Acknowledgments | 33 |
| References | 33 |
| 2. Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data | 39 |
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| 2.1 Introduction | 39 |
| 2.2 Semiconductor Manufacturing and Data Acquisition | 42 |
| 2.3 Selected Soft-Computing Methods | 52 |
| 2.4 Experiments and Results | 73 |
| 2.5 Proposed System Architecture | 80 |
| 2.6 Conclusions | 82 |
| Acknowledgments | 83 |
| References | 83 |
| 3. Clustering and Visualization of Retail Market Baskets | 87 |
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| 3.1 Introduction | 87 |
| 3.2 Domain-Speci.c Features and Similarity Space | 91 |
| 3.3 OPOSSUM | 93 |
| 3.4 CLUSION: Cluster Visualization | 96 |
| 3.5 Experiments | 101 |
| 3.6 System Issues | 105 |
| 3.7 Related Work | 108 |
| 3.8 Concluding Remarks | 111 |
| References | 112 |
| 4. Segmentation of Continuous Data Streams Based on a Change Detection Methodology | 115 |
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| 4.1 Introduction | 115 |
| 4.2 Change Detection in Classification Models | 117 |
| 4.3 Application Evaluation | 124 |
| 4.4 Conclusions and Future Work | 133 |
| References | 135 |
| 5. Instance Selection Using Evolutionary Algorithms: An Experimental Study | 139 |
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| 5.1 Introduction | 139 |
| 5.2 Instance Selection | 141 |
| 5.3 Survey of Instance Selection Algorithms | 145 |
| 5.4 Evolutionary Algorithms | 147 |
| 5.5 Evolutionary Instance Selection | 151 |
| 5.6 Methodology for the Experiments | 153 |
| 5.7 Analysis of the Experiments | 157 |
| 5.8 Concluding Remarks | 161 |
| References | 162 |
| 6. Using Cooperative Coevolution for Data Mining of Bayesian Networks | 165 |
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| 6.1 Introduction | 165 |
| 6.2 Background | 167 |
| 6.3 Learning Using Evolutionary Computation | 172 |
| 6.4 Proposed Algorithm | 175 |
| 6.5 Performance of CCGA | 182 |
| 6.6 Conclusion | 185 |
| Acknowledgment | 185 |
| 7. Knowledge Discovery and Data Mining in Medicine | 188 |
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| 7.1 Introduction | 188 |
| 7.2 KBANN with Structure Level Adaptation | 189 |
| 7.3 Rule Extraction by ADG | 199 |
| 7.4 Immune Multiagent Neural Networks | 203 |
| 7.5 Conclusion and Discussion | 219 |
| References | 220 |
| 8. Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network | 222 |
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| 8.1 Introduction | 222 |
| 8.2 Input Acquisition | 225 |
| 8.3 A Cascaded Architecture of a Neural Fuzzy Network with Feature Mapping (CNFM) | 230 |
| 8.4 Experimental Results | 237 |
| 8.5 Conclusions | 240 |
| 8.6 References | 241 |
| 9. Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets | 243 |
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| 9.1 Introduction | 243 |
| 9.2 Focusing Mechanism | 244 |
| 9.3 De.nition of Rules | 245 |
| 9.4 Algorithms for Rule Induction | 251 |
| 9.5 Experimental Results | 251 |
| 9.6 What Is Discovered? | 254 |
| 9.7 Rule Discovery as Knowledge Acquisition and Decision Support | 257 |
| 9.8 Discussion | 258 |
| 9.9 Conclusions | 261 |
| References | 261 |
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| Index | 263 |