Using numerous real-world examples, this book introduces and describes approaches for mining large time series data sets.

Presents a business-focused process built on a solid foundation of statistic and data mining principles for the development and implementation of risk prediction scorecards.

In this instructive guide, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM).

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.

Using SAS Enterprise Miner, this book illustrates the application and operation of decision trees in business intelligence, data mining, business analytics, prediction, and knowledge discovery.

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.

Packed with examples from an array of industries, this introductory text provides you with excellent starting points and practical guidelines to begin data mining today.

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.

Step by step, you'll be able to compare manual calculations with the calculations that are performed by SAS Enterprise Miner. Addresses: data collection and data cleaning, decision trees and regression trees, logistic regression models, and more.