Learn how to develop models for classification, prediction, and customer segmentation with the help of "Data Mining for Business Intelligence". In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. "Data Mining for Business Intelligence", which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models. Featuring 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 data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding.
"Data Mining for Business Intelligence" provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis. It features a business decision-making context for these key methods. It illustrates the application and interpretation of these methods using real business cases and data. This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.
- ISBN13 9781118304969
- Publish Date 11 May 2012 (first published 2 January 2007)
- Publish Status Out of Print
- Out of Print 3 April 2016
- Publish Country US
- Imprint John Wiley & Sons Inc
- Edition 2nd Revised edition
- Format eBook
- Pages 428
- Language English
- URL http://wiley.com/remtitle.cgi?isbn=1118304969