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Editorial Reviews
Data mining is a hot technology, and this short, authoritative guide shows how it works and why it is gaining ground in the worlds of finance, manufacturing, marketing, and health care. The book begins by exploring the links between "big data"--the data warehouse built up of multiple databases--and traditional statistics. (The authors defend the methods of big data against traditional statistics, which has usually relied on smaller samples. However, they also look at the sources of error in both disciplines.)
The authors then look at the nuts and bolts of the data-mining process. They show how data must be prepared--sometimes reduced--in order to be manageable, and they define the important features. They show how the actual analysis of data mining can be as simple as adding up scores for selected features or how it can use statistical methods or even neural networks. (For some problems, the features themselves aren't known ahead of time; data mining can be used to discover these features automatically.) The authors then discuss how to interpret the results of analysis so that predictions can be made for new cases based on old ones.
The book concludes with short scenarios of how data mining can be applied, with examples drawn from manufacturing, health care, marketing, and publishing. The authors show the strengths--and limits--of data mining and argue that faster hardware and greater database storage capabilities will make this technology more widely used. Though it is written by two researchers in the field, Predictive Data Mining is suitable for general readers who are interested in the topic. --Richard V. Dragan
Review
"I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and
data miners."
--Emanuel Parzen, Distinguished Professor of Statistics, Texas A&M University
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