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.Книга посвящена быстро развивающемуся разделу анализа данных - Data Mining, то есть поиску закономерностей в больших массивах данных. Включает в себя сводку теоретических принципов, основные алгоритмы, включая предназначенные для обработки данных на естественном языке, подробный предназначенный для самостоятельного изучения разбор примеров этого метода в разных отраслях и рекомендации по применению Data Mining. Для студентов, аспирантов и преподавателей, а также изучающих эту методику самостоятельно.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