Nisbet R., Elder J., Miner G. Handbook of Statistical Analysis & Data Mining Applications

Burlington: Elsevier Inc., 2009. — 860 p. — ISBN: 978-0-12-374765-5.The book is devoted to the rapidly developing section of data analysis - Data Mining, that is, the search for patterns in large data sets. Includes a summary of theoretical principles, basic algorithms, including those designed for natural language data processing, a detailed self-study case study of this method in various industries, and recommendations for the application of Data Mining. For students, graduate students and teachers, as well as those who study this technique on their own.History of Phases of Data Analysis, Basic Theory, and The Data Mining Process
The Background for Data Mining Practice
Theoretical Considerations for Data Mining
The Data Mining Process
Data Understanding and Preparation
Feature Selection
Accessory Tools for Doing Data Mining
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
Basic Algorithms for Data Mining: A Brief Overview
Advanced Algorithms for Data Mining
Text Mining and Natural Language Processing
The Three Most Common Data Mining Software Tools
Classification
Numerical Prediction
Model Evaluation and Enhancement
Medical Informatics
Bioinformatics
Customer Response Modeling
Fraud Detection
Tutorials — Step-By-Step Case Studies as a Starting Point to Learn How to Do Data Mining Analyses
How to Use Data Miner Recipe
Data Mining for Aviation Safety
Predicting Movie Box-Office Receipts
Detecting Unsatisfied Customers: A Case Study
Credit Scoring
Churn Analysis
Text Mining: Automobile Brand Review
Predictive Process Control: QC-Data Mining
Business Administration in a Medical Industry
Clinical Psychology: Making Decisions about Best Therapy for a Client
Education–Leadership Training for Business and Education
Dentistry: Facial Pain Study
Profit Analysis of the German Credit Data
Predicting Self-Reported Health Status Using Artificial Neural Networks
Measuring True Complexity, The “Right Model for The Right Use,” Top Mistakes, and The Future of Analytics
Model Complexity (and How Ensembles Help)
The Right Model for the Right Purpose: When Less Is Good Enough
Top 10 Data Mining Mistakes
Prospects for the Future of Data Mining and Text Mining as Part of Our Everyday Lives
Summary: Our Design