Books

Analyzing Receiver Operating Characteristic Curves with SAS
by Mithat Gonen
October 2007

In this example-driven book, author Mithat Gönen illustrates the many existing SAS procedures that can be tailored to produce ROC curves and expands upon further analyses using other SAS procedures and macros. Both parametric and nonparametric methods for analyzing ROC curves are covered in detail.

CRM Segmentation and Clustering Using SAS Enterprise Miner
by Randall S. Collica
June 2007

Understanding the customer is critical to your company's success. In this instructive guide, Randy Collica employs SAS Enterprise Miner and the most commonly available techniques for customer relationship management (CRM). You will learn how to segment customers more intelligently and to achieve, or at least get closer to, the one-to-one customer relationship that today's businesses want. Step-by-step examples and exercises clearly illustrate the concepts of segmentation and clustering in the context of CRM.

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications
by Kattamuri Sarma
February 2007

Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications demonstrates how to make the fullest use of SAS Enterprise Miner software. Dr. Sarma provides an in-depth explanation of the methodology and the theory behind each tool that he covers, and then shows you how the software performs the tasks. Step by step, you'll be able to compare manual calculations with the calculations that are performed by SAS Enterprise Miner.

Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring
by Naeem Siddiqi
October 2005

This book 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. It describes how risk scorecards can be a powerful tool for risk managers who need to improve the bottom line in their organizations as well as how they can develop and implement these scorecards using internal resources.

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition
by Michael Berry and Gordon Linoff
April 2004

Packed with more than 40 percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems. Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support. The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining. More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining.

Data Preparation for Analytics Using SAS
by Gerhard Svolba
November 2006

Written for anyone involved in the data preparation process for analytics, this user-friendly text offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view.

Decision Trees for Business Intelligence and Data Mining: Using SAS Enterprise Miner
by Barry de Ville
October 2006

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. It explains in detail the use of decision trees as a data mining technique and how this technique complements and supplements data mining approaches such as regression, as well as other business intelligence applications that incorporate tabular reports, OLAP, or multidimensional cubes. Examples show how various aspects of decision trees are constructed, how they operate, how to interpret them, and how to use them in a range of predictive and descriptive applications. The examples are drawn from the areas of purchase behavior, risk assessment, and business-to-business marketing.

Introduction to Data Mining Using SAS Enterprise Miner
by Patricia Cerrito
November 2006

If you have an abundance of data, but no idea what to do with it, this book was written for you! 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. The author encourages you to think of data mining as a process of exploration rather than as a collection of tools to investigate data. In that way, you choose the methods that will extract the most information from your data, and, while there are no right answers to investigating data sets, there are many questions that can be asked to produce meaningful results. Each answer then creates a path that helps you drill down to explore the data fully. It is up to you to determine what is of interest and what is important to analyze.

Managing Data Mining: Advice from Experts
by Stephan Kudyba
April 2004

This book is a collection of leading business applications in the data mining and multivariate modeling spectrum provided by experts in the field at leading U.S. corporations. Each contributor provides valued insights as to the importance quantitative modeling provides in helping their corresponding organizations manage risk, increase productivity, and drive profits in the market in which they operate.

Mastering Data Mining: The Art and Science of Customer Relationship Management
by Michael Berry and Gordon Linoff
December 1999

In this follow-up to their successful first book, Data Mining Techniques, Berry and Linoff offer a case-study-based guide to best practices in commercial data mining. This book shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management. In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, the authors share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries.

Multivariate Data Reduction and Discrimination with SAS Software
by Ravindra Khattree and Dayanand Naik
May 2000

Multivariate data commonly encountered in a variety of disciplines is easy to understand with the approaches and methods described in this book. The conceptual developments, theory, methods, and subsequent data analyses are presented systematically and in an integrated manner. The data analysis is performed using many multivariate analysis components available in SAS software. Illustrations are provided using an ample number of real data sets drawn from a variety of fields, and special care is taken to explain the SAS codes and the interpretation of corresponding outputs.

Principles of Data Mining
by David J. Hand, Heikki Mannila, and Padhraic Smyth
August 2001

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.