A Practical Guide to Data Mining for Business and Industry: Case Studies and Methodology

“A Practical Guide to Data Mining for Business and Industrygives practical tools on how information can be extracted from masses of data. The book is very well written, in a conversational tone that makes it enjoyable to read. The authors are excellent communicators. If you are interested in learning about data mining, learning to do a particular task in data mining, looking for a textbook to use in a data mining or analytics course, or have a problem or data analytic task you are working on, this book would be an excellent place to start.” (Mathematical Association of America, 23 August 2014)

A Practical Guide to Data Mining for Business and Industry

A Practical Guide to Data Mining for Business and Industry presents a user friendly approach to data mining methods and provides a solid foundation for their application. The methodology presented is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. This book is designed so that the reader can cross-reference a particular application or method to sectors of interest. The necessary basic knowledge of data mining methods is also presented, along with sector issues relating to data mining and its various applications.

A Practical Guide to Data Mining for Business and Industry:

• Equips readers with a solid foundation to both data mining and its applications
• Provides tried and tested guidance in finding workable solutions to typical business problems
• Offers solution patterns for common business problems that can be adapted by the reader to their particular areas of interest
• Focuses on practical solutions whilst providing grounding in statistical practice
• Explores data mining in a sales and marketing context, as well as quality management and medicine
• Is supported by a supplementary website (www.wiley.com/go/data_mining) featuring datasets and solutions

Aimed at statisticians, computer scientists and economists involved in data mining as well as students studying economics, business administration and international marketing.