Items related to Predictive Data Mining: A Practical Guide (with Software)
Synopsis
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. A state-of-the-art data-mining software kit accompanies the book. The software, which is delivered through a special web site, is a collection of routines for efficient mining of big data. Both classical and the more computationally expensive state-of-the-art prediction methods are included. Using a standard spreadsheet data format, this kit implements all of the data-mining tasks described in the book. The software is available for Windows 95/NT and Unix platforms (no need to specify when ordering).
"synopsis" may belong to another edition of this title.
Review
"o: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
From the Back Cover
Note: If you already own Predictive Data Mining: A Practical Guide, please click here to order the accompanying software. To order the book/software package, please click here.
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.
+ 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.
+ Illustrates the data-mining process and its potential pitfalls through real-life case studies.
"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|Note: If you already own Predictive Data Mining: A Practical Guide, please click here to order the accompanying software. To order the book/software package, please click here.
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.
+ 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.
+ Illustrates the data-mining process and its potential pitfalls through real-life case studies.
"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
"About this title" may belong to another edition of this title.
- PublisherMorgan Kaufmann Publishers In
- Publication date1997
- ISBN 10 1558604782
- ISBN 13 9781558604780
- BindingPaperback
- LanguageEnglish
- Number of pages228
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