Information-Statistical Data Mining

Warehouse Integration with Examples of Oracle Basics
2004
ISBN: 978-1-4020-7650-3
Verlag: Springer US

Buch, Englisch, Band 757, 289 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1370 g

Reihe: The Springer International Series in Engineering and Computer Science

Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is written to introduce basic concepts, advanced research techniques, and practical solutions of data warehousing and data mining for hosting large data sets and EDA. This book is unique because it is one of the few in the forefront that attempts to bridge statistics and information theory through a concept of patterns.
Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.

Zielgruppe

Research

Autoren/Hrsg.

Gupta, Arjun K.

Sy, Bon K.

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Kontinuumsmechanik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Informationstheorie, Kodierungstheorie
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenkompression, Dokumentaustauschformate
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Zeichen- und Zahlendarstellungen
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

1. Preview: Data Warehousing/Mining.- 1. What is Summary Information?.- 2. Data, Information Theory, Statistics.- 3. Data Warehousing/Mining Management.- 4. Architecture, Tools and Applications.- 5. Conceptual/Practical Mining Tools.- 6. Conclusion.- 2. Data Warehouse Basics.- 1. Methodology.- 2. Conclusion.- 3. CONCEPT OF PATTERNS & VISUALIZATION.- 1. Introduction.- Appendix: Word Problem Solution.- 4. Information Theory & Statistics.- 1. Introduction.- 2. Information Theory.- 3. Variable Interdependence Measure.- 4. Probability Model Comparison.- 5. Pearson’s Chi-Square Statistic.- 5. Information and Statistics Linkage.- 1. Statistics.- 2. Concept Of Information.- 3. Information Theory And Statistics.- 4. Conclusion.- 6. Temporal-Spatial Data.- 1. Introduction.- 2. Temporal-Spatial Characteristics.- 3. Temporal-Spatial Data Analysis.- 4. Problem Formulation.- 5. Temperature Analysis Application.- 6. Discussion.- 7. Conclusion.- 7. Change Point Detection Techniques.- 1. Change Point Problem.- 2. Information Criterion Approach.- 3. Binary Segmentation Technique.- 4. Example.- 5. Summary.- 8. Statistical Association Patterns.- 1. Information-Statistical Association.- 2. Conclusion.- 9. Pattern Inference & Model Discovery.- 1. Introduction.- 2. Concept Of Pattern-Based Inference.- 3. Conclusion.- Appendix: Pattern Utility Illustration.- 10. Bayesian Nets & Model Generation.- 1. Preliminary Of Bayesian Networks.- 2. Pattern Synthesis for Model Learning.- 3. Conclusion.- 11. Pattern Ordering Inference: Part I.- 1. Pattern Order Inference Approach.- 2. Bayesian Net Probability Distribution.- 3. Bayesian Model: Pattern Embodiment.- 4. RLCM for Pattern Ordering.- 12. Pattern Ordering Inference: Part II.- 1. Ordering General Event Patterns.- 2. Conclusion.- Appendix I: 51Largest PR(ADHJBCEF % MathType!MTEF1!+-
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$$ \overline G \underline I $$.- Appendix II: Ordering of PR(LI/SE). SE=F G I.- Appendix III.A: Evaluation of Method A.- Appendix III.B: Evaluation of Method B.- Appendix III.C: Evaluation of Method C.- 13. Case Study 1: Oracle Data Warehouse.- 1. Introduction.- 2. Background.- 3. Challenge.- 4. Illustrations.- 5. Conclusion.- Appendix I: Warehouse Data Dictionary.- 14. Case Study 2: Financial Data Analysis.- 1. The Data.- 2. Information Theoretic Approach.- 3. Data Analysis.- 4. Conclusion.- 15. Case Study 3: Forest Classification.- 1. Introduction.- 2. Classifier Model Derivation.- 3. Test Data Characteristics.- 4. Experimental Platform.- 5. Classification Results.- 6. Validation Stage.- 7. Effect of Mixed Data on Performance.- 8. Goodness Measure for Evaluation.- 9. Conclusion.- References.