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:
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.