Mr. Collica received a B.S. degree in electronic engineering from Northern Arizona University in 1982. He has 16 years of experience in the semiconductor manufacturing industry working on yield and product and quality engineering. From 1998 to 2010 he was with Compaq and Hewlett-Packard as a Sr. Business Analyst using Data Mining techniques for customer analytics in the Corp Customer Intelligence department. He is currently a Sr. Solutions Architect for SAS Institute supporting the Communications, Entertainment/Media and High-tech Manufacturing industries. His current interests are in Clustering and Ensemble models, knowledge and data engineering, missing data and imputation, and text mining techniques for use in business and customer intelligence. He has authored and co-authored 11 articles and a book - Customer Segmentation and Clustering Using SAS Enterprise Miner, Second Edition. Mr. Collica is also a member of the International Institute of Forecasters and a past member of the IEEE.
As an example, when using ordinary least squares regression (OLS), if your response or dependent attribute’s residuals are not normally distributed, your analysis is likely going to be affected and typically not in a good way. The farther away your input data is from normality, the impact on your model
In my new book, I explain how segmentation and clustering can be accomplished in three ways: coding in SAS, point-and-click in SAS Visual Statistics, and point-and-click in SAS Visual Data Mining and Machine Learning using SAS Model Studio. These three analytical tools allow you to do many diverse types of
