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.

Table of Contents:
PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process 1. History – The Phases of Data Analysis throughout the Ages 2. Theory 3. The Data Mining Process 4. Data Understanding and Preparation 5. Feature Selection – Selecting the Best Variables 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 7. Basic Algorithms 8: Advanced Algorithms 9. Text Mining 10. Organization of 3 Leading Data Mining Tools 11. Classification Trees = Decision Trees 12. Numerical Prediction (Neural Nets and GLM) 13. Model Evaluation and Enhancement 14. Medical Informatics 15. Bioinformatics 16. Customer Response Models 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. 18: Paradox of Ensembles and Complexity 19: The Right Model for the Right Use 20: The Top 10 Data Mining Mistakes 21: Prospect for the Future – Developing Areas in Data Mining 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)