However you approach it, data mining is the best collection of techniques you have for making the most out of the data you’ve already gathered. As long as you apply the correct logic, and ask the right questions, you can walk away with conclusions that have the potential to revolutionize your enterprise.
Data Mining Techniques, Third Edition
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Chapter 19: Derived Variables: Making the Data Mean More
Download this chapter from Data Mining Techniques, Third Edition, by Gordon Linoff and Michael Berry, and learn how to create derived variables, which allow the statistical modeling process to incorporate human insights. As much art as science, selecting variables for modeling is “one of the most creative parts of the data mining process,” according to the authors.
The chapter begins with a story about modeling customer attrition in the cell phone industry, moves to a review of several classic variable combinations, and then offers guidelines for the creation of derived variables.
“The best data miners and modelers rely on intuition as well as expertise. Visual exploration is the best way to develop intuition for what is going on in a data set.”
– Michael Berry
Co-Founder, Data Miners Inc.
Co-Founder, Data Miners Inc.
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Data Mining: Concepts and Techniques
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- Author: Jiawei Han,Jian Pei,Micheline Kamber
- Publisher: Elsevier
- ISBN: 9780123814807
- Category: Computers
- Page: 744
- View: 6597
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data