Anonymous
Posted Wed Aug 18 00:00:00 EDT 2004
As storage technologies continue to improve and lessen in cost, data mining is becoming an increasingly important activity for all types of enterprises and industries. I have read and reviewed many books in this field, but none have presented the key concepts as well as new research together with examples for their proper use as this book. I highly recommend this book for anyone and everyone interested in this field.
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Posted Sun Mar 28 00:00:00 EST 2004
No matter one is a beginner or an expert in the field of data mining, 'Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics' is really a great hand book. The book is written in plain English to explain the terminologies and the concept of patterns for data mining --- making it easier for readers to understand the basic and advanced data mining concepts. Also, it shows clearly how data mining theories are realized in software implementation, how the implementation is applied to a variety of data sets in different disciplines, and how one can gain knowledge from the valuable information obtained from the process of data mining. Furthermore, it also discussed the way to interpret and to evaluate the quality of information resulting from the process of data mining. I particularly like the real world case study examples that help me to understand the data mining principles discussed in the book, and to draw me in further into the field. This book is well organized. I strongly recommend the book!
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More About This Textbook
Overview
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.
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Table of Contents
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 G I). 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. Software Tool for Singly Connected Bayesian Model.