Advanced Techniques in Knowledge Discovery and Data Mining (eBook)

Nikhil Pal (Herausgeber)

eBook Download: PDF

2007 | 2005
XII, 256 Seiten
Springer London (Verlag)
978-1-84628-183-9 (ISBN)

Infos

Clear and concise explanations to understand the learning paradigms.

Chapters written by leading world experts.

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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: "e;As the term suggests, data mining is the analysis of data to establish relationships and identify patterns"e; [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].

Inhalt

Contents 6
Preface 7
1. Trends in Data Mining and Knowledge Discovery 13
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
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
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
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
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
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
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
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
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
Index 263

Produkt-Details

• Erscheint lt. Verlag: 31.12.2007
• Reihe/Serie: Advanced Information and Knowledge Processing
• Reihe/Serie: Advanced Information and Knowledge Processing
• Zusatzinfo: XII, 256 p.
• Verlagsort: London
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Informatik ► Theorie / Studium ► Algorithmen
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: Algorithm analysis and problem complexity • algorithms • Architecture • Bayesian Network • Calculus • classification • Clustering • Database • Databases • Data Mining • data structures • Evolution • evolutionary algorithm • evolutionary algorithms • Information Technologies • Internet • Knowledge Databases • Knowledge Discovery • Networks • Neural Fuzzy Networks • Visualization
• ISBN-10: 1-84628-183-0 / 1846281830
• ISBN-13: 978-1-84628-183-9 / 9781846281839