SAS^® Enterprise Miner^™ Papers and Presentations

Feature Extraction Methods for Time Series Data in SAS Enterprise Miner
Lee, Taiyeong; Zhang, Ruiwen; Xiao, Yongqiao; Dean, Jared; SAS Institute, Inc. 2014
This paper describes various feature extraction methods for time series data that are implemented in SAS Enterprise Miner.
Link Analysis Using SAS Enterprise Miner
Liu, Ye; Zhang, Ruiwen; Dean, Jared; SAS Institute, Inc. 2014
This paper first explains the concepts of association discovery, sequence discovery, multiple centrality measures and clustering coefficient measure, and item clusters. Then it shows how the Link Analysis node incorporates these concepts in analyzing transactional data. The paper also shows how you can adapt non-transactional data to the link analysis framework. Finally, examples illustrate how to use the Link Analysis node to analyze Netflix data and Fisher’s Iris data.

Leveraging Ensemble Models in SAS Enterprise Miner
Maldonado, Miguel; Dean, Jared; Czika, Wendy; Haller, Susan; SAS Institute, Inc. 2014
This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup that enables you to take fullest advantage of the ensemble capabilities of SAS Enterprise Miner by using existing nodes, Start Groups and End Groups nodes, and custom coding.
An Overview of Machine Learning with SAS Enterprise Miner
Hall, Patrick; Dean, Jared; Kabul, Ilknur Kaynar; Silva, Jorge; SAS Institute, Inc. 2014
This paper provides an overview of machine learning and presents several supervised and unsupervised machine learning examples that use SAS Enterprise Miner. Download the zip file
What’s New in SAS Enterprise Miner 13.1
Dean, Jared; Wexler, Jonathan; SAS Institute, Inc. 2014
This paper reviews SAS Enterprise Miner 13.1, which focuses on these three themes; it provides 10 new nodes, three new procedures, and algorithmic and technological enhancements.

Incremental Response Modeling Using SAS Enterprise Miner
Lee, Taiyeong; Zhang, Ruiwen; Meng, Xiangxiang; Ryan, Laura; SAS Institute, Inc. 2013
This paper shows how to find a profitable customer group (ones likely to buy or respond positively to marketing campaigns when they are targeted)and how to maximize return on investment by using SAS Enterprise Miner.

It's About Time: Discrete Time Survival Analysis Using SAS Enterprise Miner
Schubert, Sascha; Haller, Susan; Lee, Taiyeong; SAS Institute, Inc. 2012
This paper briefly explains the theory of survival analysis and provides an introduction to its implementation in SAS Enterprise Miner.

Ratemaking Using SAS Enterprise Miner: An Application Study
Anderson, Billie; SAS Institute, Inc. 2011
This paper ties the theory of ratemaking using Generalized linear models (GLMs) to case studies that use real insurance data and shows you how to use SAS Enterprise Miner to model claim frequency, severity, and pure premiums.
Time Series Data Mining with SAS Enterprise Miner
Schubert, Sascha; Lee, Taiyeong ; SAS Institute, Inc. 2011
This paper introduces the new time series data mining techniques that will be integrated into SAS Enterprise Miner 7.1.

The Power of the Group Processing Facility in SAS Enterprise Miner
Schubert, Sascha; SAS Institute, Inc. 2010
The group processing facility in SAS Enterprise Miner is useful when your data can be segmented or grouped, and you want to process the grouped data in different ways. It uses BY-group processing to process observations from one or more data sources that are grouped or ordered by values of one or more common variables.

Predictive Models Based on Reduced Input Space That Uses Rejected Variables
Lee, Taiyeong; Duling, David; Latour, Dominique ; SAS Institute, Inc. 2009
This paper demonstrates the use of projection methods to combine both the selected variable space and the rejected variable space.
The Next Generation: SAS Enterprise Miner 6.1
Thompson, Wayne; Duling, David; SAS Institute, Inc. 2009
This paper provides an overview of the SAS Enterprise Miner 6.1 new features and uses an example of mining data about charitable donations to illustrate some of these features.

Tailoring the Use of SAS Enterprise Miner
Schubert, Sascha; SAS Institute, Inc. 2008
This paper discusses how the new generation of SAS Enterprise Miner 5 and the SAS stored process facility provide an easy way to tailor data mining functionality to the user's needs.
Cool New Features in SAS Enterprise Miner
Duling, David; Thompson, Wayne; Schubert, Sascha; SAS Institute, Inc. 2008
This paper will focus on the new features in Enterprise Miner 5.3 with analytical examples.
Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry
Cross, Gendon, AT&T Corporation; Thompson, Wayne; SAS Institute, Inc. 2008
This paper outlines several segmentation techniques using SAS Enterprise Miner.
Two-Stage Variable Clustering for Large Data Sets
Lee, Taiyeong; Duling, David; Liu, Song; Latour, Dominique; SAS Institute 2008
This paper introduces the two-stage, variable clustering technique for large data sets. This technique uses global clusters, sub-clusters, and their principal components.

Identifying and Overcoming Common Data Mining Mistakes
Wielenga, Doug; SAS Institute, Inc. 2007
This paper discusses how to identify and overcome several common modeling mistakes.
Critical Success Factors for Rate Making with SAS Enterprise Miner
Khopkar, Hrushikesh; King, Jay; SAS Institute, Inc. 2007
This paper attempts to tie the theory of rate making to technology and business definitions and describes how to set a benchmark for validation and customer handover to ensure a successful rate-making project.

Papers are in Portable Document Format (PDF) and can be viewed with the free Adobe Acrobat Reader.

Powerpoint presentations and SAS programs can be downloaded as zip files.

SAS® Enterprise Miner™ Papers and Presentations