Lexi Hawks

8月 132018
 

Data in the cloud makes it easily accessible, and can help businesses run more smoothly. SAS Viya runs its calculations on Cloud Analytics Service (CAS). David Shannon of Amadeus Software spoke at SAS Global Forum 2018 and presented his paper, Come On, Baby, Light my SAS Viya: Programming for CAS. (In addition to being an avid SAS user and partner, David must be an avid Doors fan.) This article summarizes David's overview of how to run SAS programs in SAS Viya and how to use CAS sessions and libraries.

If you're using SAS Viya, you're going to need to know the basics of CAS to be able to perform calculations and use SAS Viya to its potential. SAS 9 programs are compatible with SAS Viya, and will run as-is through the CAS engine.

Using CAS sessions and libraries

Use a CAS statement to kick off a session, then use CAS libraries (caslibs) to store data and resources. To start the session, simply code "cas;" Each CAS session is given its own unique identifier (UUID) that you can use to reconnect to the session.

Handpicked Related VIDEO: SAS programming in the cloud: CASL code

There are a few significant codes that can help you to master CAS operations. Consider these examples, based on a CAS session that David labeled "speedyanalytics":

  • What CAS sessions do I have running?
    cas _all_ list;
  • Get the version and license specifics from the CAS server hosting my session:
    cas speedyanalytics listabout;
  • I want to sign out of SAS Studio for now, so I will disconnect from my CAS session, but return to it later…
    cas speedyanalytics disconnect;
  • ...later in the same or different SAS Studio session, I want to reconnect to the CAS session I started earlier using the UUID I previous grabbed from the macro variable or SAS log:
    cas uuid="&speedyanalytics_uuid";
  • At the end of my program(s), shutdown all my CAS sessions to release resources on the server:
    cas _all_ terminate;

Using CAS libraries

CAS libraries (caslib) are the method to access data that is being stored in memory, as well as the related metadata.

From the library, you can load data into CAS tables in a couple of different ways:

  1. Takes a sample data set, calculate a new measure and stores the output in memory
  2. Proc COPY can bring existing SAS data into a caslib
  3. Proc CASUTIL loads tables into caslibs

The Proc CASUTIL allows you to save your tables (named "classsi" data in David's examples) for future use through the SAVE statement:

proc casutil;
 save casdata="classsi" casout="classsi";
run;

And reload like this in a future session, using the LOAD statement:

proc casutil;
 load casdata="classsi" casout="classsi";
run;

When accessing your CAS libraries, remember that there are multiple levels of scope that can apply. "Session" refers to data from just the current session, whereas "Global" allows you to reach data from all CAS sessions.

Programming in CAS

Showing how to put CAS into action, David shared this diagram of a typical load/save/share flow:

Existing SAS 9 programs and CAS code can both be run in SAS Viya. The calculations and data memory occurs through CAS, the Cloud Analytics Service. Before beginning, it's important to understand a general overview of CAS, to be able to access CAS libraries and your data. For more about CAS architecture, read this paper from CAS developer Jerry Pendergrass.

The performance case for SAS Viya

To close out his paper, David outlined a small experiment he ran to demonstrate performance advantages that can be seen by using SAS Viya v3.3 over a standard, stand-alone SAS v9.4 environment. The test was basic, but performed reads, writes, and analytics on a 5GB table. The tests revealed about a 50 percent increase in performance between CAS and SAS 9 (see the paper for a detailed table of comparison metrics). SAS Viya is engineered for distributive computing (which works especially well in cloud deployments), so more extensive tests could certainly reveal even further increases in performance in many use cases.

Additional resources

A quick introduction to CAS in SAS Viya was published on SAS Users.

8月 022018
 

SAS Viya has opened an entirely new set of capabilities, allowing SAS to analyze on cloud technology in real-time. One of the best new features of SAS Viya is its ability to pair with open source platforms, allowing developers the freedom of language and implementation to integrate with the power of SAS analytics.

At SAS Global Forum 2018, Sean Ankenbruck and Grace Heyne Lybrand from Zencos Consulting led the talk, SAS Viya: The Beauty of REST in Action. While the paper – and this blog post – outlines the use of Python and SAS Viya, note that SAS Viya integrates with R, Java and Lua as well.

Nonetheless, this Python integration example shows how easy it is to integrate SAS Viya and open source technologies. Here is the basic workflow:

1. A developer creates a web application, in a language of their choice
2. A user enters data in the web application
3. The collected data that is passed to Viya via the defined APIs
4. Analysis is performed in Viya using SAS actions
5. Results are passed back to the web application
6. The web application presents the results to the user

About the process

SAS’ Cloud Analytic Services (CAS) acts as a server to analyze data, and REST API’s are being used to integrate many programming languages into SAS Viya. REST stands for Representational State Transfer, and is a set of constraints that allows scalability and integration of multiple web-based systems. In layman’s terms, it’s a set of software design patterns that provides handy connector points from one web app to another. The REST API is what developers use to interact with and submit requests through the processing system.

CAS actions are what allow “tasks” to be completed on SAS Viya. These “tasks” are under the categories of Statistics, Analytics, System, and Data Mining and Machine Learning.

Integration with Python

To access CAS through Python, the SAS Scripting Wrapper for Analytics Transfer (SWAT) package is used, letting Python conventions dictate CAS actions. To create this interface, data must be captured through a web application in a format that Python can transmit to SAS Viya.
In order to connect Python and CAS, the following is necessary:

• Hostname
• CAS Port
• Username
• Password

Let’s see it in action

As an example, one project about wine preferences used CAS-collected data through a questionnaire stored in Python’s Pandas library. When the information was gathered, the decision tree was uploaded to SAS Viya. A model was created with common terms reviewers use to describe wines, feeding into a decision tree. The CAS server scored the users’ responses in real-time, and then sent the results back to the user providing them with suggested wines to match their inputs.

Process for model

Code to utilize tree:

conn.loadactionset("decisionTree") 
conn.decisionTree.dTreeTrain( 
      casOut = {"name":"tree_model"},
      inputs = [{vars}], 
      modelId = "DT_wine_variety", 
      table = {"caslib":"public", "name": "wines_model_data"}, 
      target = "variety")

"Decision" given to user

Conclusion

SAS Viya has opened SAS to a plethora of opportunities, allowing many different programming languages to be interpreted and quickly integrated, giving analysts and data scientists more flexibility.

Additional resources

At Your Service: Using SAS® Viya™ and Python to Create Worker Programs for Real-Time Analytics, Jon Klopfer, Scott Koval, and Mia List
SAS Viya
sas-viya-programming on github
python-swat on github
SAS Global Forum

Additional SAS Viya talks from SAS Global Forum

A Need for Speed: Loading Data via the Cloud, Henry Christoffels
Come On, Baby, Light my SAS® Viya®: Programming for CAS, David Shannon
Just Enough SAS® Cloud Analytic Services: CAS Actions for SAS® Visual Analytics Report Developers, Michael Drutar
Running SAS Viya on Oracle Cloud without Sacrificing Performance, Dan Grant
Command-Line Administration in SAS® Viya®, Danny Hamrick
Five Approaches for High-Performance Data Loading to the SAS® Cloud Analytic Services Server, Rob Collum

How SAS Viya uses REST APIs to integrate with Python was published on SAS Users.

6月 092018
 

SAS Studio is the latest way you can access SAS. This newer interface allows users to reach SAS through a web browser, offering a number of unique ways that SAS can be optimized. At SAS Global Forum 2018, Lora Delwiche (SAS) and Susan J Slaughter (Avocet Solutions) gave the presentation, “SAS Studio: A New Way to Program in SAS.” This post reviews the paper, offering you insights of how to enhance your SAS Studio programming performance.

This new interface is a popular one, as it is included in Base SAS and used for SAS University Edition and SAS OnDemand for Academics. It can be considered a self-serving system, since you write programs in SAS Studio itself that are then processed through SAS and delivered results. Its ease of accessibility from a range of computers is putting it in high demand – which is why you should learn how to optimize its use.

How to operate

A SAS server processes your coding and returns the results to your browser, in order to make the programs run successfully. By operating in Programmer mode, you are given the capabilities to view Code, Log, and Results. On the right side of the screen you can write your code, and the toolbar allows you to access the many different tools that are offered.

SAS Studio

Libraries are used to access your SAS data sets, where you can also see the variables contained in each set. You can create your own libraries, and set the path for your folder through SAS Studio.

In order to view each data set, the navigation pane can also be used. Right click on the data set name and select “Open” to access files through this method. These datasets can be adjusted in a number of ways: columns can be shifted around by dragging the headings; column sizes can be adjusted; the top right corner has arrows to view more information; clicking on the column heading will sort that data.

 

In order to control your data easily, filters can be used. Filters are accessed by right-clicking the column heading and selecting the filter that best fits your needs.

How to successfully code

A unique feature to SAS Studio is its code editor that will automatically format your code. Clicking on the icon will properly format each statement and put it on its own line. Additionally, syntax help pops up as you type to give you possible suggestions in your syntax, a tool that can be turned on or off through the Preferences window.

One tool that’s particularly useful is the snippet tool, where you can copy and paste frequently used code.

Implementing and Results

After code is written, the Log tool can help you review your code, whereas Results will generate your code carried out after it has been processed. The Results tab will give you shareable items that can be saved or printed for analysis purposes.

Conclusion

These insights offer just a glimpse of all of the capabilities in programming through SAS Studio. Through easy browser access, your code can be shared and analyzed with a few clicks.

Additional Resources

Additional SAS Global Forum Proceedings
SAS Studio Videos
SAS Studio Courses
SAS Studio Programming Starter Guide
SAS Studio Blogs
SAS Studio Community

Other SAS Global Forum Programming Papers of Interest

Code Like It Matters: Writing Code That's Readable and Shareable
Paul Kaefer

Identifying Duplicate Variables in a SAS ® Data Set
Bruce Gilsen

Macros I Use Every Day (And You Can, Too!)
Joe DeShon

Merge with Caution: How to Avoid Common Problems when Combining SAS Datasets
Joshua M. Horstman

SAS Studio: A new way to program in SAS was published on SAS Users.

6月 022018
 

The SAS PlatformFor software users and SAS administrators, the question often becomes how to streamline their approach into the easiest to use system that most effectively completes the task at hand. At SAS Global Forum 2018, the topic of a “Big Red Button” was an idea that got audience members asking – is there a way to have just a few clicks complete all the stages of the software administration lifecycle? In this article, we review Sergey Iglov’s SAS Global Forum paper A ‘Big Red Button’ for SAS Administrators: Myth or Reality?” to get a better understanding of what this could look like, and how it could change administrators’ jobs for the better. Iglov is a director at SASIT Limited.

What is a “Big Red Button?”

With the many different ways the SAS Platform can be utilized, there is a question as to whether there is a single process that can control “infrastructure provisioning, software installation and configuration, maintenance, and decommissioning.” It has been believed that each of these steps has a different process; however, as Iglov concluded, there may be a way to integrate these steps together with the “Big Red Button.”

This mystery “button” that Iglov talked about would allow administrators to easily add or delete parts of the system and automate changes throughout; thus, the entire program could adapt to the administrator’s needs with a simple click.

Software as a System –SAS Viya and cloud based technologies

Right now, SAS Viya is compatible with the automation of software deployment processes through a centralized management. Right now, SAS Viya is compatible with a centralized automated deployment process. Through insights easily created and shared on the cloud, SAS Viya stands out, as users can access a centrally hosted control panel instead of needing individual installations.

Using CloudFormation by Amazon Web Services

At this point, the “Big Red Button” points toward systems such as CloudFormation. CloudFormation allows users of Amazon Web Services to lay out the infrastructure needed for their product visually, and easily make changes that will affect the software. As Iglov said, “Once a template is deployed using CloudFormation it can be used as a stack to simplify resources management. For example, when a stack is deleted all related resources are deleted automatically as well.”

Conclusion

Connecting to SAS Viya, CloudFormation can install and configure the system, and make changes. This would help SAS administrators adapt the product to their needs, in order to derive intelligence from data. While the future potential to use a one-click button is out there for many different platforms, using cloud based software and programs such as CloudFormation enable users to go through each step of SAS Platform’s administration lifecycle efficiently and effectively.

Additional Resources

SAS Viya Brochure
Sergey Iglov: "A 'Big Red Button' for SAS administrators: Myth or Reality?"

Additional SAS Global Forum 2018 talks of interest for SAS Administrators

A Programming Approach to Implementing SAS® Metadata-Bound Libraries for SAS® Data Set Encryption Deepali Rai, SAS Institute Inc.

Command-Line Administration in SAS® Viya®
Danny Hamrick, SAS

External Databases: Tools for the SAS® Administrator
Mathieu Gaouette, Prospective MG inc.

SAS® Environment Manager – A SAS® Viya® Administrator’s Swiss Army Knife
Michelle Ryals, Trevor Nightingale, SAS Institute Inc.

Troubleshooting your SAS® Grid Environment
Jason Hawkins, Amadeus Software Limited

Multi-Factor Authentication with SAS® and Symantec VIP
Jody Steadman, Mike Roda, SAS Institute Inc.

OpenID Connect Opens the Door to SAS® Viya® APIs
Mike Roda, SAS Institute Inc.

Understanding Security for SAS® Visual Analytics 8.2 on SAS® Viya®
Antonio Gianni, Faisal Qamar, SAS Institute Inc.

Latest and Greatest: Best Practices for Migrating to SAS® 9.4
Alec Fernandez, Leigh Fernandez, SAS Institute Inc.

Planning for Migration from SAS® 9.4 to SAS® Viya®
Don B. Hayes, DLL Consulting Inc.; Spencer Hayes, Cached Consulting LLC; Michael Shealy, Cached Consulting LLC; Rebecca Hayes, Green Peach Consulting Inc.

SAS® Viya®: Architect for High Availability Now and Users Will Thank You Later
Jerry Read, SAS Institute Inc.

Taming Change: Bulk Upgrading SAS® 9.4 Environments to a New Maintenance Release
Javor Evstatiev, Andrey Turlov

Is there a “Big Red Button” to use The SAS Platform? was published on SAS Users.

5月 302018
 

SAS Enterprise Miner has been a leader in data mining and modeling for over 20 years. The system offers over 80 different nodes that help users analyze, score and model their data. With a wide range of functionalities, there can be a number of different ways to produce the results you want.

At SAS® Global Forum 2018, Principal Systems Engineer Melodie Rush spoke about her experience with SAS® Enterprise Miner™, and compiled a list of hints that she believe will help users of all levels. This article previews her full presentation, Top 10 Tips for SAS Enterprise Miner Based on 20 Years’ Experience. The paper includes images and further details of each of the tips noted below; I’d encourage you to check it out to learn more.

Top Ten Tips for Enterprise Miner

Tip 1: How to find the node you’re looking for

If you struggle finding the node that best fits what you need, there’s a system that can simplify it.

Nodes are organized by Sample, Explore, Modify, Model, and Assess. Find which of these best describes what you are trying to do, and scroll across each node alphabetically for a description.

Tip 2: Add node from diagram workspace

Double click any node on the toolbar to see its properties. An example of the results this presents are shown below:

Top Ten Tips for Enterprise Miner

Tip 3: Clone a process flow

Highlight process flow by dragging your mouse across, right-click or CTRL+C, and Paste or CTRL+V where you want to insert process flow.

Tip 4: New features

  • There’s a new tab, HPDM (High-Performance Data Mining), which contains several new nodes that cover data mining and machine learning algorithms.
  • There are two new nodes under Utility that incorporate Open Source and SAS Viya.
  • The Open Source Integration node allows you to use R language code in SAS Enterprise Miner diagrams.
  • A SAS Viya Code node now incorporates code that will be used in SAS Viya and CAS, and algorithms from SAS Visual Data Mining and Machine Learning.
  • To save and share your results, there are now the Register Model and Save Data nodes under Utility.
  • You can now register models to the SAS Metadata Server to score or compare easily.
  • A Save Data node lets you save training, validation, test, score, or transaction data as SAS, JMP, Excel, CSV or tab-delimited files.

Tip 5: The unknown node

The reporter node under Utility allows you to easily document your Enterprise Miner process flow diagrams. A .pdf or .rtf is created with an image of the process flow.

Tip 6: The node that changes everything

The Metadata node, on the Utility tab, allows you to change metadata information and values in your diagram. You also can capture settings to then apply to data in another diagram.

Tip 7: How to generate a scorecard

A scorecard emphasizes what variables and values from your model are important. Values are reported on a 0 to 1,000 scale, with the higher being more likely the event you’re measuring occurs. To do this, have the Reporter node follow a Score node, and then change the Nodes property to Summary under Reporter node properties.

Tip 8: How to override the 512 level limit

If faced with the error message, “Maximum target levels of 512 exceeded,” your input is resulting in more than 512 distinct results. To get around this, you need to change EM_TRAIN_MAXLEVELS to another value. To do so, either change the macro value in properties

or change the macro value in project start code.

Tip 9: Which variable selection method should I use?

Instead of choosing just one variable selection method, you can combine different ones such as Decision Trees, Forward, Chi-Square, and others. The results can be combined using different selection properties, such as None (no changes made from original metadata), Any (reject a variable if any previous variable selection nodes reject it), All (reject a variable if all of the previous variable selection nodes reject it), and Majority (reject a variable if the majority of the variable selection nodes reject it).

Tip 10: Interpreting neural network

Decision trees can be produced to interpret networks, by changing the Prediction variable to be your Target and the Target variable to be rejected.

Conclusion

With so many options to create models that best suit your preferences, these tips will help sharpen your focus and allow you to use SAS Enterprise Miner more efficiently and effectively. This presentation was one in a series of talks on Enterprise Miner tool presented at SAS® Global Forum 2018.

Additional Resources

SAS Enterprise Miner
SAS Enterprise Learning Tutorials
Getting Started With SAS Enterprise Miner Tutorial Videos

Additional SAS Enterprise Miner talks from Global Forum 2018

A Case Study of Mining Social Media Data for Disaster Relief: Hurricane Irma
Bogdan Gadidov, Linh Le, Analytics and Data Science Institute, Kennesaw State University

A Study of Modelling Approaches for Predicting Dropout in a Business College
Xuan Wang, Helmut Schneider, Louisiana State University

Analysis of Nokia Customer Tweets with SAS® Enterprise Miner™ and SAS® Sentiment Analysis Studio
Vaibhav Vanamala MS in Business Analytics, Oklahoma State University

Analysis of Unstructured Data: Topic Mining & Predictive Modeling using Text
Ravi Teja Allaparthi

Association Rule Mining of Polypharmacy Drug Utilization Patterns in Health Care Administrative Data Using SAS® Enterprise Miner™
Dingwei Dai, Chris Feudtner, The Children’s Hospital of Philadelphia

Bayesian Networks for Causal Analysis
Fei Wang and John Amrhein, McDougall Scientific Ltd.

Classifying and Predicting Spam Messages Using Text Mining in SAS® Enterprise Miner™
Mounika Kondamudi, Oklahoma State University

Image Classification Using SAS® Enterprise Miner 14.1

Model-Based Fiber Network Expansion Using SAS® Enterprise Miner™ and SAS® Visual Analytics
Nishant Sharma, Charter Communications

Monte Carlo K-Means Clustering SAS Enterprise Miner
Donald K. Wedding, PhD Director of Data Science Sprint Corporation

Retail Product Bundling – A new approach
Bruno Nogueira Carlos, Youman Mind Over Data

Using Market Basket Analysis in SAS® Enterprise MinerTM to Make Student Course Enrollment Recommendations
Shawn Hall, Aaron Osei, and Jeremiah McKinley, The University of Oklahoma

Using SAS® Enterprise Miner for Categorization of Customer Comments to Improve Services at USPS
Olayemi Olatunji, United States Postal Service Office of Inspector General

Top 10 tips for SAS Enterprise Miner based on 20 years’ experience was published on SAS Users.

5月 302018
 

SAS Enterprise Miner has been a leader in data mining and modeling for over 20 years. The system offers over 80 different nodes that help users analyze, score and model their data. With a wide range of functionalities, there can be a number of different ways to produce the results you want.

At SAS® Global Forum 2018, Principal Systems Engineer Melodie Rush spoke about her experience with SAS® Enterprise Miner™, and compiled a list of hints that she believe will help users of all levels. This article previews her full presentation, Top 10 Tips for SAS Enterprise Miner Based on 20 Years’ Experience. The paper includes images and further details of each of the tips noted below; I’d encourage you to check it out to learn more.

Top Ten Tips for Enterprise Miner

Tip 1: How to find the node you’re looking for

If you struggle finding the node that best fits what you need, there’s a system that can simplify it.

Nodes are organized by Sample, Explore, Modify, Model, and Assess. Find which of these best describes what you are trying to do, and scroll across each node alphabetically for a description.

Tip 2: Add node from diagram workspace

Double click any node on the toolbar to see its properties. An example of the results this presents are shown below:

Top Ten Tips for Enterprise Miner

Tip 3: Clone a process flow

Highlight process flow by dragging your mouse across, right-click or CTRL+C, and Paste or CTRL+V where you want to insert process flow.

Tip 4: New features

  • There’s a new tab, HPDM (High-Performance Data Mining), which contains several new nodes that cover data mining and machine learning algorithms.
  • There are two new nodes under Utility that incorporate Open Source and SAS Viya.
  • The Open Source Integration node allows you to use R language code in SAS Enterprise Miner diagrams.
  • A SAS Viya Code node now incorporates code that will be used in SAS Viya and CAS, and algorithms from SAS Visual Data Mining and Machine Learning.
  • To save and share your results, there are now the Register Model and Save Data nodes under Utility.
  • You can now register models to the SAS Metadata Server to score or compare easily.
  • A Save Data node lets you save training, validation, test, score, or transaction data as SAS, JMP, Excel, CSV or tab-delimited files.

Tip 5: The unknown node

The reporter node under Utility allows you to easily document your Enterprise Miner process flow diagrams. A .pdf or .rtf is created with an image of the process flow.

Tip 6: The node that changes everything

The Metadata node, on the Utility tab, allows you to change metadata information and values in your diagram. You also can capture settings to then apply to data in another diagram.

Tip 7: How to generate a scorecard

A scorecard emphasizes what variables and values from your model are important. Values are reported on a 0 to 1,000 scale, with the higher being more likely the event you’re measuring occurs. To do this, have the Reporter node follow a Score node, and then change the Nodes property to Summary under Reporter node properties.

Tip 8: How to override the 512 level limit

If faced with the error message, “Maximum target levels of 512 exceeded,” your input is resulting in more than 512 distinct results. To get around this, you need to change EM_TRAIN_MAXLEVELS to another value. To do so, either change the macro value in properties

or change the macro value in project start code.

Tip 9: Which variable selection method should I use?

Instead of choosing just one variable selection method, you can combine different ones such as Decision Trees, Forward, Chi-Square, and others. The results can be combined using different selection properties, such as None (no changes made from original metadata), Any (reject a variable if any previous variable selection nodes reject it), All (reject a variable if all of the previous variable selection nodes reject it), and Majority (reject a variable if the majority of the variable selection nodes reject it).

Tip 10: Interpreting neural network

Decision trees can be produced to interpret networks, by changing the Prediction variable to be your Target and the Target variable to be rejected.

Conclusion

With so many options to create models that best suit your preferences, these tips will help sharpen your focus and allow you to use SAS Enterprise Miner more efficiently and effectively. This presentation was one in a series of talks on Enterprise Miner tool presented at SAS® Global Forum 2018.

Additional Resources

SAS Enterprise Miner
SAS Enterprise Learning Tutorials
Getting Started With SAS Enterprise Miner Tutorial Videos

Additional SAS Enterprise Miner talks from Global Forum 2018

A Case Study of Mining Social Media Data for Disaster Relief: Hurricane Irma
Bogdan Gadidov, Linh Le, Analytics and Data Science Institute, Kennesaw State University

A Study of Modelling Approaches for Predicting Dropout in a Business College
Xuan Wang, Helmut Schneider, Louisiana State University

Analysis of Nokia Customer Tweets with SAS® Enterprise Miner™ and SAS® Sentiment Analysis Studio
Vaibhav Vanamala MS in Business Analytics, Oklahoma State University

Analysis of Unstructured Data: Topic Mining & Predictive Modeling using Text
Ravi Teja Allaparthi

Association Rule Mining of Polypharmacy Drug Utilization Patterns in Health Care Administrative Data Using SAS® Enterprise Miner™
Dingwei Dai, Chris Feudtner, The Children’s Hospital of Philadelphia

Bayesian Networks for Causal Analysis
Fei Wang and John Amrhein, McDougall Scientific Ltd.

Classifying and Predicting Spam Messages Using Text Mining in SAS® Enterprise Miner™
Mounika Kondamudi, Oklahoma State University

Image Classification Using SAS® Enterprise Miner 14.1

Model-Based Fiber Network Expansion Using SAS® Enterprise Miner™ and SAS® Visual Analytics
Nishant Sharma, Charter Communications

Monte Carlo K-Means Clustering SAS Enterprise Miner
Donald K. Wedding, PhD Director of Data Science Sprint Corporation

Retail Product Bundling – A new approach
Bruno Nogueira Carlos, Youman Mind Over Data

Using Market Basket Analysis in SAS® Enterprise MinerTM to Make Student Course Enrollment Recommendations
Shawn Hall, Aaron Osei, and Jeremiah McKinley, The University of Oklahoma

Using SAS® Enterprise Miner for Categorization of Customer Comments to Improve Services at USPS
Olayemi Olatunji, United States Postal Service Office of Inspector General

Top 10 tips for SAS Enterprise Miner based on 20 years’ experience was published on SAS Users.