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Predictive Data Mining : A Practical Guide

Part of the The Morgan Kaufmann Series in Data Management Systems series
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The potential business advantages of data mining are well documented in publications for executives and managers.

However, developers implementing major data-mining systems need concrete information about the underlying technical principles and their practical manifestations in order to either integrate commercially available tools or write data-mining programs from scratch.

This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.

It: focuses on the preparation and organization of data and the development of an overall strategy for data mining; reviews sophisticated prediction methods that search for patterns in big data; describes how to accurately estimate future performance of proposed solutions; and, illustrates the data-mining process and its potential pitfalls through real-life case studies.

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£54.99
Product Details
1558604030 / 9781558604032
Paperback / softback
006.312
08/12/1997
United States
English
xii, 228 p. : ill.
23 cm
academic/professional/technical Learn More
Reprint. Transferred to digital printing.
* Focuses on the preparation and organization of data and the development of an overall strategy for data mining.<p/>* Reviews sophisticated prediction methods that search for patterns in big data.<p/>* Describes how to accurately estimate future performance of proposed solutions.<p/>* Illustrates the data-mining process and its potential pitfalls through real-life case studies.
* Focuses on the preparation and organization of data and the development of an overall strategy for data mining.<p/>* Reviews sophisticated prediction methods that search for patterns in big data.<p/>* Describes how to accurately estimate future performance of proposed solutions.<p/>* Illustrates the data-mining process and its potential pitfalls through real-life case studies. UF Business applications, UNC Data capture & analysis