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HANDBOOK OF STATISTICAL ANALYSIS AND DATA MINING APPLICATIONS
Handbook of Statistical Analysis and Data Mining Applications
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By
Robert Nisbet, Pacific Capital Bank Corporation, Santa Barbara, CA, USA
John Elder, Elder Research, Inc. and the University of Virginia, Charlottesville, USA
Gary Miner, StatSoft, Inc., Tulsa, OK, USA

Description


The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.



Audience
Business analysts, scientists, engineers, researchers, and students in statistics and data mining

Contents


Preface
Forwards (Dean Abbott and Tony Lachenbruch)
Introduction



PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process

Chapter 1. History – The Phases of Data Analysis throughout the Ages
Chapter 2. Theory
Chapter 3. The Data Mining Process
Chapter 4. Data Understanding and Preparation
Chapter 5. Feature Selection – Selecting the Best Variables
Chapter 6: Accessory Tools and Advanced Features in Data



PART II: - The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools

Chapter 7. Basic Algorithms
Chapter 8: Advanced Algorithms
Chapter 9. Text Mining
Chapter 10. Organization of 3 Leading Data Mining Tools
Chapter 11. Classification Trees = Decision Trees
Chapter 12. Numerical Prediction (Neural Nets and GLM)
Chapter 13. Model Evaluation and Enhancement
Chapter 14. Medical Informatics
Chapter 15. Bioinformatics
Chapter 16. Customer Response Models
Chapter 17. Fraud Detection



PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses

Listing of Guest Authors of the Tutorials
Tutorials within the book pages:
How to use the DMRecipe
Aviation Safety using DMRecipe
Movie Box-Office Hit Prediction using SPSS CLEMENTINE
Bank Financial data – using SAS-EM
Credit Scoring                                                                                                                                                 CRM Retention using CLEMENTINE
Automobile – Cars – Text Mining
Quality Control using Data Mining
Three integrated tutorials from different domains, but all using C&RT to predict and display possible structural relationships among data:
Business Administration in a Medical Industry
Clinical Psychology? Finding Predictors of Correct Diagnosis                                                                                    Education – Leadership Training: for Business and Education                                                                               Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book
Listing of Tutorials on Accompanying CD



PART IV: Paradox of Complex Models; using the ?right model for the right use?, on-going development, and the Future.

Chapter 18: Paradox of Ensembles and Complexity
Chapter 19: The Right Model for the Right Use
Chapter 20: The Top 10 Data Mining Mistakes
Chapter 21: Prospect for the Future – Developing Areas in Data Mining
Chapter 22: Summary

GLOSSARY of STATISICAL and DATA MINING TERMS
INDEX
CD – With Additional Tutorials, data sets, Power Points, and Data Mining software (STATISTICA Data Miner & Text Miner & QC-Miner – 90 day free trial)



Bibliographic details
Hardbound, 864 pages, publication date: MAY-2009
ISBN-13: 978-0-12-374765-5
Imprint: ACADEMIC PRESS

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EUR 63.95
GBP 54.99
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Last update: 22 Sep 2009
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