Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, Second Edition
- 6h 47m
- Galit Shmueli, Nitin R. Patel, Peter C. Bruce
- John Wiley & Sons (US)
- 2010
Data Mining for Business Intelligence, Second Edition uses real data and actual cases to illustrate the applicability of data mining (DM) intelligence in the development of successful business models. Featuring complimentary access to XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of DM techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples, now doubled in number in the second edition, are provided to motivate learning and understanding. This book helps readers understand the beneficial relationship that can be established between DM and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions. New topics include detailed coverage of visualization (enhanced by Spotfire subroutines) and time series forecasting, among a host of other subject matter.
About the Authors
GALIT SHMUELI, PhD, is Associate Professor of Statistics and Director of the eMarkets Research Lab in the Robert H. Smith School of Business at the University of Maryland. Dr. Shmueli is the coauthor of Statistical Methods in e-Commerce Research and Modeling Online Auctions, both published by Wiley.
NITIN R. PATEL, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology for over ten years.
PETER C. BRUCE is President and owner of statistics.com, the leading provider of online education in statistics.
In this Book
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Foreword
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Introduction
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Overview of the Data Mining Process
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Data Visualization
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Dimension Reduction
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Evaluating Classification and Predictive Performance
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Multiple Linear Regression
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k-Nearest Neighbors (k-NN)
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Naive Bayes
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Classification and Regression Trees
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Logistic Regression
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Neural Nets
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Discriminant Analysis
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Association Rules
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Cluster Analysis
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Handling Time Series
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Regression-Based Forecasting
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Smoothing Methods
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Cases
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References
