This 270-page book draft (PDF) by Galit Shmueli, Nitin R. Patel, and Peter C. Bruce was based on a data mining course at MIT's Sloan School of Management. This book is intended for the business student (and practitioner) of data mining techniques, and its goal is threefold:
- To provide both a theoretical and practical understanding of the key methods of classification, prediction, reduction and exploration that are at the heart of data mining;
- To provide a business decision-making context for these methods;
- Using real business cases, to illustrate the application and interpretation of these methods.
An important feature of this book is the use of Excel, and all required data mining algorithms (plus illustrative datasets) are provided in an Excel add-in, XLMiner (now distributed by Frontline Solvers), which offers a large variety of data mining tools.
The contents include
- Overview of the Data Mining Process
- Data Exploration and Dimension Reduction
- Evaluating Classification and Predictive Performance
- Multiple Linear Regression
- Three Simple Classification Methods
- Classification and Regression Trees
- Logistic Regression
- Neural Nets
- Discriminant Analysis
- Association Rules
- Cluster Analysis
A more recent version of this book is
Data Mining for Business Intelligence, 2nd Edition.
The site above also has Datasets and Instructor Materials.