Synopses & Reviews
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.
- 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 using Statistica, SAS and SPSS software
- 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
Review
The essential professional reference for data mining applications and statistical analysis.
Review
“Great introduction to the real-world process of data mining. The overviews, practical advise, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners.”
- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)
If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner.
- Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World
Synopsis
This comprehensive professional reference for scientists, engineers, and researchers brings together in a single resource all the information a beginner will need to rapidly learn how to conduct data mining and the statistical analysis required to interpret the data once mined. A glossary of data mining terms provided in the appendix.
Table of 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)