1. Data Mining For Business Intelligence 2nd Edition Download

This book is intended for the business student (and practitioner) of data mining techniques, and all data mining algorithms are provided in an Excel add-in XLMiner.

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:

1. 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;
2. To provide a business decision-making context for these methods;
3. 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.

Following the first edition (2006), the Indian edition (2008) and the Korean edition (2009), we are glad to announce the new-and-improved 2nd edition. In this edition we've added a set of chapters on forecasting time series, as well as a full chapter on data visualization with an emphasis on data mining and on interactive.

• The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques.
• The official textbook companion website, with datasets, instructor material, and more.

The contents include

Data Mining For Business Intelligence 2nd Edition Download

• 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

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Data Mining for Business Intelligence, 2nd Edition.

The site above also has Datasets and Instructor Materials.