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