In his previous book, Data Preparation for Data Mining, Dorian Pyle addressed data mining.s most overlooked issue. In this his latest book, he tackles its most challenging. Business Modeling and Data Mining presents a clear, broadly applicable methodology for taking verbally expressed business problems and transforming them into well-defined, quantitative models that, through mining, yield solutions. Significantly, it also closes the loop by showing readers how to take mining results based on a given model and interpret them in terms of the original business problem.

These are major feats, but Pyle delivers even more. As they sharpen their modeling skills, readers will learn to recognize the kinds of problems data mining is especially good at solving. They will also learn how examining a problem correctly contributes significantly to its solution.for example, by uncovering hidden assumptions and relationships. Finally, the reader will gain a broader sense of the uses of data mining, learning how it can be successfully applied to strategic problems, not just tactical ones.

• Written for data mining consultants, internal and external, and managers whose work depends on drawing sound conclusions from data.
• Dramatically narrows the gap between the technical world of data mining and the business world that depends on it.
• Addresses a critical issue in terms that will help readers apply data mining to a wider range of problems, both tactical and strategic - and get better results.
• Uses only as much theory as necessary to make its methodology easy to adapt and follow.
• Includes thorough, step-by-step methodology for business modeling and data mining
• Additional resources at Dorian.s website, www.modelandmine.com

Mr. Pyle has over 20 years experience in artificial intelligence and machine learning techniques which are used in what is known today as .data mining.. He has applied this knowledge as a consultant with Naviant and Thinking Machines Corporation and with various companies directly involved in credit card marketing for banks and with manufacturing companies using industrial automation. In 1976 he was involved in building artificially intelligent machine learning systems utilizing the pioneering technologies that are currently known as neural computing and associative memories. He is current in and familiar with using the most advanced technologies in data mining including: entropic analysis (information theory), chaotic and fractal decomposition, neural technologies, evolution and genetic optimization, algebra evolvers, case-based reasoning, concept induction and other advanced statistical techniques. He is the author of Data Preparation for Data Mining, ©.99 Morgan Kaufmann Publishing.