Handbook of Statistical Analysis and Data Mining Applications

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

Table of Contents

Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
Chapter 1: The Background for Data Mining Practice
Chapter 2: Theoretical Considerations for Data Mining
Chapter 3: The Data Mining Process
Chapter 4: Data Understanding and Preparation
Chapter 5: Feature Selection
Chapter 6: Accessory Tools for Doing Data Mining

Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Specialized areas using data mining

Chapter 7: Basic Algorithms for Data Mining: A Brief Overview
Chapter 8: Advanced Algorithms for Data Mining
Chapter 9: Text Mining and Natural Language Processing
Chapter 10: The Three Most Common Data Mining Software Tools
Chapter 11: Classification
Chapter 12: Numerical Prediction
Chapter 13: Model Evaluation and Enhancement
Chapter 14: Medical Informatics
Chapter 15: Bioinformatics
Chapter 16: Customer Response Modeling
Chapter 17: Fraud Detection

Part 3: Tutorials-Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses
Guest Authors of the Tutorials

Part 4: Measuring true complexity, the “Right Model for the Right Use,” Top Mistakes, and the Future of Analytics
Chapter 18: Model Complexity (and How Ensembles Help)
Chapter 19: The Right Model for the Right Purpose: When Less Is Good Enough
Chapter 20: Top 10 Data Mining Mistakes
Chapter 21: Prospects for the Future of Data Mining and Text Mining as Part of Our Everyday Lives
Chapter 22: Summary: Our Design