IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

by Keith McCormick
Pub. Date:
Packt Publishing


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IBM SPSS Modeler Cookbook

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork.

IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art.

Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace.

Go beyond the basics and get the full power of your data mining workbench with this practical guide.

Product Details

ISBN-13: 9781849685467
Publisher: Packt Publishing
Publication date: 11/24/2013
Pages: 382
Sales rank: 1,050,710
Product dimensions: 7.50(w) x 9.25(h) x 0.79(d)

About the Author

Keith McCormick is the Vice President and General Manager of QueBIT Consulting's Advanced Analytics team. He brings a wealth of consulting/training experience in statistics, predictive modeling and analytics, and data mining. For many years, he has worked in the SPSS community, first as an External Trainer and Consultant for SPSS Inc., then in a similar role with IBM, and now in his role with an award winning IBM partner. He possesses a BS in Computer Science and Psychology from Worcester Polytechnic Institute.

He has been using Stats software tools since the early 90s, and has been training since 1997. He has been doing data mining and using IBM SPSS Modeler since its arrival in North America in the late 90s. He is an expert in IBM's SPSS software suite including IBM SPSS Statistics, IBM SPSS Modeler (formally Clementine), AMOS, Text Mining, and Classification Trees. He is active as a moderator and participant in statistics groups online including LinkedIn's Statistics and Analytics Consultants Group. He also blogs and reviews related books at He enjoys hiking in out of the way places, finding unusual souvenirs while traveling overseas, exotic foods, and old books.

Dean Abbott is the President of Abbott Analytics, Inc. in San Diego, California. He has over two decades experience in applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, customer acquisition and retention, digital behavior for web applications and mobile, customer lifetime value, survey analysis, donation solicitation and planned giving. He has developed, coded, and evaluated algorithms for use in commercial data mining and pattern recognition products, including polynomial networks, neural networks, radial basis functions, and clustering algorithms for multiple software vendors.

He is a seasoned instructor, having taught a wide range of data mining tutorials and seminars to thousands of attendees, including PAW, KDD, INFORMS, DAMA, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. He also has taught both applied and hands-on data mining courses for major software vendors, including IBM SPSS Modeler, Statsoft STATISTICA, Salford System SPM, SAS Enterprise Miner, IBM PredictiveInsight, Tibco Spotfire Miner, KNIME, RapidMiner, and Megaputer Polyanalyst.

Meta S. Brown helps organizations use practical data analysis to solve everyday business problems. A hands-on analyst who has tackled projects with up to $900 million at stake, she is a recognized expert in cutting-edge business analytics.

She is devoted to educating the business community on effective use of statistics, data mining, and text mining. A sought-after analytics speaker, she has conducted over 4000 hours of seminars, attracting audiences across North America, Europe, and South America. Her articles appear frequently on All Analytics, Smart Data Collective, and other publications. She is also co-author of Big Data, Mining and Analytics: Key Components for Strategic Decisions (forthcoming from CRC Press, Editor: Stephan Kudyba).

She holds a Master of Science in Nuclear Engineering from the Massachusetts Institute of Technology, a Bachelor of Science in Mathematics from Rutgers University, and professional certifications from the American Society for Quality and National Association for Healthcare Quality. She has served on the faculties of Roosevelt University and National-Louis University.

Tom Khabaza is an independent consultant in predictive analytics and data mining, and the Founding Chairman of the Society of Data Miners. He is a data mining veteran of over 20 years and many industries and applications. He has helped to create the IBM SPSS Modeler (Clementine) data mining workbench and the industry standard CRISP-DM methodology, and led the first integrations of data mining and text mining. His recent thought leadership includes the 9 Laws of Data Mining.

Scott R. Mutchler is the Vice President of Advanced Analytics Services at QueBIT Consulting LLC. He had spent the first 17 years of his career building enterprise solutions as a DBA, software developer, and enterprise architect. When Scott discovered his true passion was for advanced analytics, he moved into advanced analytics leadership roles where he was able to drive millions of dollars in incremental revenues and cost savings through the application of advanced analytics to most challenging business problems. His strong IT background turned out to be a huge asset in building integrated advanced analytics solutions.

Recently, he was the Predictive Analytics Worldwide Industrial Sector Lead for IBM. In this role, he worked with IBM SPSS clients worldwide. He architected advanced analytic solutions for clients in some of the world's largest retailers and manufacturers.

He received his Masters from Virginia Tech in Geology. He stays in Colorado and enjoys an outdoor lifestyle, playing guitar, and travelling.

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IBM SPSS Modeler Cookbook 3.5 out of 5 based on 0 ratings. 2 reviews.
Anonymous More than 1 year ago
Keith McCormick has done an exceptional job detailing how to effectively use SPSS Modeler for all level of data miners. The book is well organized , with early chapters laying the foundation for the more advanced modelling techniques that are presented in later chapters. Each chapter is easy-to-follow, broken down into digestible steps and presented with corresponding pictures. Each chapter also summarizes the potential use for the recipe and later expounds on other potential uses and next steps for each recipe. I personally read the SPSS Cookbook for a specific purpose and didn't read the chapters sequentially, but nonetheless found the recipes both relevant to my endeavor as well as incredibly useful and clear. For anyone interested in augmenting their data mining knowledge within SPSS, I would highly recommend Keith's book.
Anonymous More than 1 year ago
Review can change depending on my overall experience once completing the book, but right off the bat, none of the recipes is 100% accurate and I'm only part of the way through Chapter 1. Source files are not named correctly and the directions for the recipes reference putting nodes downstream, but do not specify EXACTLY where to place them downstream so most of your time is spent troubleshooting the recipe and trying to interpret what the authors actually mean. The numbers referenced in the book are different than the numbers you get when you run the streams. I started submitting errata through the appropriate channels requested by the web site that  offers the digital version of the book (which I also purchased) and after a few submissions, I got an email stating that I needed to send the errata directly to the authors. There are errors in every recipe thus far and I would most of my time sending in errors and deciphering meaning within the recipes, not worth my time. This concept has the potential to be a boon of knowledge, but navigating the mechanics severely detracts from the learning that can take place.