Randall S. Collica - Segmentation Analytics with SAS Viya

An Approach to Clustering and Visualization

Randall S. Collica

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The correct bibliographic citation for this manual is as follows: Collica, Randall S. 2021. Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization . Cary, NC: SAS Institute Inc.

Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization

Copyright 2021, SAS Institute Inc., Cary, NC, USA

ISBN 978-1-952363-06-1 (Hardcover)
ISBN 978-1-951684-05-1 (Paperback)
ISBN 978-1-951684-06-8 (Web PDF)
ISBN 978-1-951684-07-5 (EPUB)
ISBN 978-1-951684-08-2 (Kindle)

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Contents

About This Book

What Does This Book Cover?

The main purpose of this book is to demonstrate how to accomplish clustering and segmentation in SAS Viya through the use of several graphical user interfaces (SAS Visual Statistics, Model Studio, SAS Visual Analytics, and a coding interface of SAS Studio). This is a how to book with practical, real data examples in each chapter. This book also covers visualizations of clustering including a relatively new technique called t-SNE that can be used to better understand the underlying structure of the data before and after clustering.

While this book does not cover the theory of clustering and segmentation, there are a good number of available references at the end of each chapter where you can find appropriate additional materials that are relevant to each chapter.

Is This Book for You?

This book is designed for analysts, data miners, and data scientists who need to use the all in-memory platform of SAS Viya for the purposes of clustering and segmentation. I have not attempted to write a comprehensive book on segmentation, but this book is focused on the analytics and methods of SAS Viya actions, procedures, and even the SAS 9.4 procedures that can be used in conjunction with SAS Viya through SAS Studio. If you are a novice with clustering and segmentation, a chapter in the Appendix is designed for the basic understanding of how clustering works with distances of observations. If you already know clustering well, then this book will aid you in how to accomplish clustering and segmentation analytics using SAS Viya.

What Are the Prerequisites for This Book?

While it will be helpful if you are already familiar with clustering concepts, it isnt mandatory as Appendix 2 provides a discussion on the basic concepts of distance and how that is used in many clustering algorithms. An understanding of basic analytics concepts such as linear regression, elementary probability, statistics, and machine learning will be helpful.

What Should You Know about the Examples?

Each example is real data that has been anonymized and is available for your use to understand how to apply each of the methods described in this book. The results that you obtain while executing each of the examples might differ from what is printed in this manuscript. The results might differ because of sampling proportions, distributed computing environments versus single computing, or other general options settings that can affect the results. One way to ensure that the results keep consistent in a distributed computing environment is to use the SAS Viya CAS node= restrictions that limit the processing to a single node. This will likely keep the results consistent; however, it may defeat the purpose of larger data sets where distributed computing is typically used to reduce execution times. Please see the NWORKERS= Session Option section in the SAS Cloud Analytic Services: Users Guide concerning the CAS number of workers options available at: https:// go.documentation.sas.com/?docsetId=casref&docsetTarget=n1v9x1q6ll09ypn0zknvo54rk9ya.htm&docsetVersion=v_001&locale=en .

Software Used to Develop the Books Content

The software used in this book is SAS Viya version 3.5 and 4.0. (4.0 is the containerized deployment of SAS Viya and is labeled as 2020.1.x versions.)

Example Code and Data

This book includes tutorials for you to follow to gain hands-on experience with SAS. Each folder contains the data sets, code, and macros for each chapter except for .

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About The Author

Randy Collica is a Principal Pre-Sales Solutions Architect at SAS supporting the retail, communications, consumer, and media industries. His research interests include segmentation, clustering, ensemble models, missing data and imputation, Bayesian techniques, and text mining for use in business and customer intelligence. He has authored and coauthored eleven articles and two books. He holds a US patent titled System and Method of Combining Segmentation Data. He received a BS in electronic engineering from Northern Arizona University.