Information-Statistical Data Mining

Warehouse Integration with Examples of Oracle Basics

By: Bon K. Sy, Arjun K. Gupta

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Hardcover | 30 November 2003

At a Glance

Hardcover
316 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

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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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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Data Warehousing Engineering & Technology Mechanical Engineering & Materials Materials Science Mechanics of Solids Data Capture & Analysis Computer Science

Inspiration
Dedication
Contributing Authors and Contact Information
Preface
Acknowledgments
Preview: Data Warehousing/Mining
What Is Summary Information?
Data, Information Theory, Statistics
Data Warehousing/Mining Management
Architecture, Tools And Applications
Conceptual/Practical Mining Tools
Conclusion
Data Warehouse Basics
Methodology
Conclusion
Concept of Patterns & Visualization
Introduction
Appendix: Word problem solution
Information Theory & Statistics
Introduction
Information theory
Variable interdependence measure
Probability model comparison
Pearson's Chi-Square statistic
Information and Statistics Linkage
Statistics
Concept of information
Information theory and statistics
Temporal-Spatial Data
Introduction
Temporal-spatial characteristics
Temporal-spatial data analysis
Problem formulation
Temperature analysis application
Discussion
Conclusion
Change Point Detection Techniques
Change point problem
Information criterion approach
Binary segmentation technique
Example
Statistical Association Patterns
Information-Statistical Association
Conclusion
Pattern Inference & Model Discovery
Introduction
Concept of pattern-based inference
Conclusion. Appendix: Pattern utility illustration
Bayesian Nets & Model Generation
Preliminary of Bayesian Networks
Pattern Synthesis for MODEL Learning
Conclusion
Pattern Ordering Inference: Part I
Pattern Ordering Inference: Part II
Ordering General Event Patterns
Conclusion
51 largest PR(ADHJ BCE F &Gmacr; &Imacu;)
ordering Of PR(LúY/Yú SE). SE=F G I

Evaluation of Method A. Appendix III
Evaluation of Method B. Appendix III
Evaluation of Method C
Oracle Data Warehouse
Introduction
Background
Challenge
Illustrations
Conclusion
Warehouse Data Dictionary
Financial Data Analysis
The data
Information theoretic approach
data analysis
Forest Classification
Introduction
Classifier model derivation
Test data characteristics
Experimental platform
Classification results
Validation stage
Effect of mixed data on performance
Goodness measure for evaluation
Conclusion. References. Index. Web resource: http://www.techsuite.net/kluwer/
Web Accessible Scientific Data Warehouse Example
MathCAD Implementation of Change Point Detection
S-PLUS open source code for Statistical Association
Internet Downloadable Model Discovery Tool
Software Tool for Singly Connected Bayesian Model.
Table of Contents provided by Publisher. All Rights Reserved.

Information-Statistical Data Mining

Warehouse Integration with Examples of Oracle Basics

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

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