Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics

ISBN-13: 9781402076503 / Angielski / Twarda / 2003 / 289 str.

Bon K. Sy; Arjun K. Gupta; A. K. Gupta

Opis

As computer technology becomes more powerful, it becomes possible to collect data at a level, by size and the level of extent that could not even be imagined just a few years ago. At the same time, it also offers a growing possibility of discovering intelligence from data through statistical techniques cornered as Exploratory Data Analysis (EDA). While EDA evolves to play a major role in the field of data mining, treatment for temporal spatial data remains a challenge. Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics will address this issue.
This book will also attempt to address this issue through a framework that may allow us to answer at least partially, the following two important questions. First, how do we gain insights into understanding the intelligence behind the valuable information that data mining offers? More specifically, how do we interpret and evaluate the quality of information resulting from an EDA that is typically oriented around statistical techniques. Overall, 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.

Spis treści

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.