Data Mining for Business Intelligence

Praise for the First Edition
„ full of vivid and thought-provoking anecdotes needs to beread by anyone with a serious interest in research andmarketing.“ --Research magazine
„Shmueli et al. have done a wonderful job in presenting thefield of data mining a welcome addition to the literature.“ --computingreviews. com
Incorporating a new focus on data visualization and time seriesforecasting, Data Mining for Business Intelligence, SecondEdition continues to supply insightful, detailed guidance onfundamental data mining techniques. This new edition guides readersthrough the use of the Microsoft Office Excel add-in XLMiner fordeveloping predictive models and techniques for describing andfinding patterns in data.
From clustering customers into market segments and finding thecharacteristics of frequent flyers to learning what items arepurchased with other items, the authors use interesting, real-worldexamples to build a theoretical and practical understanding of keydata mining methods, including classification, prediction, andaffinity analysis as well as data reduction, exploration, andvisualization.
The Second Edition now features:

* Three new chapters on time series forecasting, introducingpopular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topicssuch as explanatory vs. predictive modeling, two-level models, andensembles * A revised chapter on data visualization that now featuresinteractive visualization principles and added assignments thatdemonstrate interactive visualization in practice * Separate chapters that each treat k-nearest neighbors andNaïve Bayes methods * Summaries at the start of each chapter that supply an outlineof key topics
The book includes access to XLMiner, allowing readers to workhands-on with the provided data. Throughout the book, applicationsof the discussed topics focus on the business problem as motivationand avoid unnecessary statistical theory. Each chapter concludeswith exercises that allow readers to assess their comprehension ofthe presented material. The final chapter includes a set of casesthat require use of the different data mining techniques, and arelated Web site features data sets, exercise solutions, PowerPointslides, and case solutions.
Data Mining for Business Intelligence, Second Edition isan excellent book for courses on data mining, forecasting, anddecision support systems at the upper-undergraduate and graduatelevels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods inthe fields of business, finance, marketing, computer science, andinformation technology.