Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

This book arose out of a data mining course at MIT's Sloan School of Management. Preparation for the course revealed that there are a number of excellent books on the business context of data mining, but their coverage of the statistical and machine learning algorithms and theoretical underpinnings is not sufficiently detailed to provide a practical guide for users who possess the raw skills and tools to analyze data. This book is intended for the business student (and practitioner) of data mining techniques, and the 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; and (3) using real business cases and data, to illustrate the application and interpretation of these methods. The book employs the use of an Excel add-in, XLMinerTM, at no cost to registered instructors, in order to illustrate and interpret the various data sets that are presented throughout. Real-life business cases are also presented so that readers can implement algorithms with a very low learning hurdle.Table of ContentsForewordPrefaceAcknowledgments1. Introduction2. Overview of the Data Mining Process3. Data Exploration and Dimension Reduction4. Evaluating Classification and Predictive Performance5. Multiple Linear Regression6. Three Simple Classification Methods7. Classification and Regression trees8. Logistic Regression9. Neural Nets10. Discriminant Analysis11. Association Rules12. Cluster Analysis13. CasesReferencesIndex