Information-Statistical Data Mining

Sy, Bon K. (Queens College/City University of New York, USA); Gupta, Arjun K.

ISBN 10: 1402076509 / ISBN 13: 9781402076503

Editore: Kluwer Academic Publishers, 2003

Riguardo questo articolo

Written to introduce basic concepts, advanced research techniques, and solutions of data warehousing and data mining for hosting large data sets and EDA, this book attempts to bridge statistics and information theory through a concept of patterns. It is aimed at a professional audience composed of researchers and practitioners in industry. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 311 pages, biography. BIC Classification: TGMD; UN. Category: (G) General (US: Trade); (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 234 x 156 x 19. Weight in Grams: 1370. . 2003. Hardback. . . . .

Codice articolo V9781402076503

Dati bibliografici

• Titolo: Information-Statistical Data Mining
• Editore: Kluwer Academic Publishers
• Data di pubblicazione: 2003
• Lingua: Inglese
• ISBN 10: 1402076509
• ISBN 13: 9781402076503
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Contenuti

Inspiration. Dedication. Contributing Authors and Contact Information. Preface. Acknowledgments. 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. 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. 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. 12: Pattern Ordering Inference: Part II. 1. Ordering General Event Patterns. 2. Conclusion. Appendix I: 51 largest PR(ADHJ BCE | F &Gmacr; &Imacu;). Appendix II: ordering Of PR(L£Y/Y£ | 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. 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. Index. Web resource: http://www.techsuite.net/kluwer/ 1. Web Accessible Scientific Data Warehouse Example. 2. MathCAD Implementation of Change Point Detection. 3. S-PLUS open source code for Statistical Association. 4. Internet Downloadable Model Discovery Tool. 5.