Pal Advanced Techniques in Knowledge Discovery and Data Mining

1. Auflage 2007
ISBN: 978-1-84628-183-9
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 256 Seiten

Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions like hospitals, research laboratories, banks, insurance companies, and retail stores and by Internet users. This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” [1]. It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis [2].

Autoren/Hrsg.

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