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.
Key Features
- Written "By Practitioners for Practitioners"
- Non-technical explanations build understanding without jargon and equations
- Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models
- Practical advice from successful real-world implementations
- Includes extensive case studies, examples, MS PowerPoint slides and datasets
- CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book
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)
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning Data Mining, Inference and Prediction (Springer Series in Statistics)/ ISBN: 978-0-387-95284-0, $94.00, 18,000 units
Ross, Introduction to Probability and Statistics for Engineers, ISBN: 978012598057, 7/2004, (8976 units LOT)
Zwillinger, The Handbook of Differential Equations, $102., ISBN: 97801278443964, Oct. 1997, 1767 units LOT
Jeffrey/Zwillinger, Table of Integrals, Series and Products, $94.95, ISBN: 9780123736376, Feb. 2007, 2447 LOT units
Ian Witten and Eibe Frank , Data Mining: Practical Machine Learning Tools and Techniques, 2e, (Morgan Kaufmann Series in Data Management Systems), $62.95, 560 pp., June 2005, LOT units 18000, ISBN: 9780120884070
Ross, Simulation, 4th ed. Aug. 2006, 312 pp, $89.95, ISBN: 9780125980630
David Olson and Dursun Delen, Advanced Data Mining Techniques, , $109.00, 6x9, 180 pp, Springer, March 2008, 978-3540769163
Han, Kamber, Data Mining, Concepts and Techniques, (Morgan Kaufmann Series in Data Management Systems) 2nd ed. , Nov. 2005, 9x7, 800pp, $64.95
Hastie, Tibshirani, Friedman, The Elements of Statistical Learning, Springer series in Statistics, $89.95, 552 pp, July 2003, (ISBN: 978-0387952840)
Wilcox, Applying Contemporary Statistical Techniques, Dec. 2002, $89.95, 608 pp, ISBN: 9780127515410
Freund, Wilson, Statistical Methods, Dec. 2002, $92.95, 673 pp, ISBN: 9780122676512
