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Below is a list of our recommended titles, organized by category. You can download the code and data for these books from the SAS Press Author Pages. Our online bookstore lists all available titles.
Interested in exploring the exciting world of data mining using SAS? Here are some great books to guide you.
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Applied Data Mining for Forecasting Using SAS By Tim Rey, Arthur Kordon, and Chip Wells
Using numerous real-world examples, this book introduces and describes approaches for mining large time series data sets.
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Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Second Edition By Kattamuri Sarma
This title addresses data collection and data cleaning, decision trees and regression trees, logistic regression models, neural networks, variable selection and variable transformation, and more.
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Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition By Randall S. Collica
In this instructive guide, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM).
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Data Preparation for Analytics Using SAS By Gerhard Svolba
This user-friendly text offers practical advice in the form of SAS coding tips and tricks, along with providing the reader with a conceptual background on
data structures and considerations from the business point of view.
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Decision Trees for Analytics Using SAS Enterprise Miner By Barry deVille and Padraic Neville
An expanded and enhanced resource which provides the most comprehensive treatment of decision tree theory, use, and applications available in one easy-to-access place. Users will be able to make recommendations from decision trees and search algorithms in order to improve and customize their segmentation of the market.
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Integrating Results through Meta-Analytic Review Using SAS Software By Morgan C. Wang, Ph.D., and Brad J. Bushman, Ph.D.
Wang and Bushman offer a thorough description of how meta-analysis can be used in data mining projects to discover meaningful relations among variables
in a collection of studies.
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Multivariate Data Reduction and Discrimination with SAS Software By Ravindra Khattree, Ph.D., and Dayanand N. Naik, Ph.D.
Presents conceptual developments, theory, methods, and subsequent data analyses in an integrated manner. Together with Applied Multivariate
Statistics with SAS Software, Second Edition, provides comprehensive information about using SAS for multivariate statistics.
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Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS By Dr. Goutam Chakraborty, Murali Pagolu, and Satish Garla
Having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected. |
