Pal / Jain Advanced Techniques in Knowledge Discovery and Data Mining

1. Auflage 2007
ISBN: 978-1-84628-183-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 256 Seiten

Reihe: Advanced Information and Knowledge Processing

Clear and concise explanations to understand the learning paradigms.
Chapters written by leading world experts.

Autoren/Hrsg.

Jain, Lakhmi

Pal, Nikhil

Inhaltsverzeichnis

1;Contents;6
2;Preface;7
3;1. Trends in Data Mining and Knowledge Discovery;13
3.1;1.1 Knowledge Discovery and Data Mining Process;13
3.2;1.2 Six-Step Knowledge Discovery and Data Mining Process;17
3.3;1.3 New Technologies;22
3.4;1.4 Future of Data Mining and Knowledge Discovery?;28
3.5;1.5 Conclusions;32
3.6;Acknowledgments;33
3.7;References;33
4;2. Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data;39
4.1;2.1 Introduction;39
4.2;2.2 Semiconductor Manufacturing and Data Acquisition;42
4.3;2.3 Selected Soft-Computing Methods;52
4.4;2.4 Experiments and Results;73
4.5;2.5 Proposed System Architecture;80
4.6;2.6 Conclusions;82
4.7;Acknowledgments;83
4.8;References;83
5;3. Clustering and Visualization of Retail Market Baskets;87
5.1;3.1 Introduction;87
5.2;3.2 Domain-Speci.c Features and Similarity Space;91
5.3;3.3 OPOSSUM;93
5.4;3.4 CLUSION: Cluster Visualization;96
5.5;3.5 Experiments;101
5.6;3.6 System Issues;105
5.7;3.7 Related Work;108
5.8;3.8 Concluding Remarks;111
5.9;References;112
6;4. Segmentation of Continuous Data Streams Based on a Change Detection Methodology;115
6.1;4.1 Introduction;115
6.2;4.2 Change Detection in Classification Models;117
6.3;4.3 Application Evaluation;124
6.4;4.4 Conclusions and Future Work;133
6.5;References;135
7;5. Instance Selection Using Evolutionary Algorithms: An Experimental Study;139
7.1;5.1 Introduction;139
7.2;5.2 Instance Selection;141
7.3;5.3 Survey of Instance Selection Algorithms;145
7.4;5.4 Evolutionary Algorithms;147
7.5;5.5 Evolutionary Instance Selection;151
7.6;5.6 Methodology for the Experiments;153
7.7;5.7 Analysis of the Experiments;157
7.8;5.8 Concluding Remarks;161
7.9;References;162
8;6. Using Cooperative Coevolution for Data Mining of Bayesian Networks;165
8.1;6.1 Introduction;165
8.2;6.2 Background;167
8.3;6.3 Learning Using Evolutionary Computation;172
8.4;6.4 Proposed Algorithm;175
8.5;6.5 Performance of CCGA;182
8.6;6.6 Conclusion;185
8.7;Acknowledgment;185
9;7. Knowledge Discovery and Data Mining in Medicine;188
9.1;7.1 Introduction;188
9.2;7.2 KBANN with Structure Level Adaptation;189
9.3;7.3 Rule Extraction by ADG;199
9.4;7.4 Immune Multiagent Neural Networks;203
9.5;7.5 Conclusion and Discussion;219
9.6;References;220
10;8. Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network;222
10.1;8.1 Introduction;222
10.2;8.2 Input Acquisition;225
10.3;8.3 A Cascaded Architecture of a Neural Fuzzy Network with Feature Mapping (CNFM);230
10.4;8.4 Experimental Results;237
10.5;8.5 Conclusions;240
10.6;8.6 References;241
11;9. Discovery of Positive and Negative Rules from Medical Databases Based on Rough Sets;243
11.1;9.1 Introduction;243
11.2;9.2 Focusing Mechanism;244
11.3;9.3 De.nition of Rules;245
11.4;9.4 Algorithms for Rule Induction;251
11.5;9.5 Experimental Results;251
11.6;9.6 What Is Discovered?;254
11.7;9.7 Rule Discovery as Knowledge Acquisition and Decision Support;257
11.8;9.8 Discussion;258
11.9;9.9 Conclusions;261
12;References;261
13;Index;263