Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel(r) with XLMiner(r) - Tapa dura

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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.

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Praise for the First Edition

" full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing."
Research magazine

"Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature."
computingreviews.com

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. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data.

From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization.

The Second Edition now features:

• Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles
• A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice
• Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods
• Summaries at the start of each chapter that supply an outline of key topics

The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions.

Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.