SASŪ Enterprise Miner
The most current release is SAS Enterprise Miner 13.1.
SAS Enterprise Miner 13.1 is a major release that runs on the first maintenance release for SAS 9.4 and later releases. Here are some of the new features and enhancements in the core user interface:
- The Open Source node enables users to integrate R language code inside a SAS Enterprise Miner process flow diagram.
- The Save Data node provides users with a simple way to save training, validation, test, score, or transaction data from a SAS Enterprise Miner path to a user-defined path, or a previously defined SAS library.
- The Decision Tree node enables users to import a previously created model and apply this model to new data.
- The Time Series Dimension Reduction node extracts features from each time series and reduces the dimension of time.
- The Time Series Correlation node helps users perform correlation and cross-correlation analyses. It calculates numerous auto-correlation and cross-correlation statistics on time series data.
- The Time Series Decomposition node enables you to perform seasonal decomposition of time series.
Here are some of the new features and enhancements in the SAS Enterprise Miner High-Performance Data Mining nodes:
- The HP Cluster node uses the high-performance HPCLUSTER procedure to perform k-means clustering analysis in distributed computing environments.
- The HP Forest node provides users with a choice of variable selection methods: Out-of-Bag (OOB) Average Error for interval targets, or OOB marginal reduction for class targets.
- The HP GLM node uses the high-performance HPGENSELECT procedure to fit a generalized linear model in a distributed computing environment.
- The HP Neural node now provides a User-Defined Architecture.
- The HP Principal Components node performs principal component analysis by using the high-performance HPPRINCOMP procedure.
- The HP Support Vector Machine node uses the newly developed high-performance HPSVM procedure for binary classification problems.
- The HP Tree node adds support for models that have interval targets.
Here are some of the new features and enhancements in the SAS Enterprise Miner high-performance procedures:
- The new HPBNET procedure learns a Bayesian network from an input data set to create a predictive model in supervised data mining.
- The new HPCLUS procedure enables you to read and write data in distributed form and to perform clustering and scoring in parallel.
- The new HPSVM procedure executes the support vector machine (SVM) algorithm in multiple threads.
- The HPFOREST procedure offers enhancements to enable the training algorithm to use multiple concurrent threads, to segregate data for pruning and early stopping, and to generate an observation ID in scored data.
- The HPNEURAL procedures now enables you to use an arbitrary number of hidden layers to support deep learning, to specify the Poisson and gamma error function and the exponential output layer activation function to support modeling of count data, and to specify an activation function for hidden layers and for the output layer.
Visit our general product information page on www.sas.com for more information.
Here are our top suggestions for new users of SAS Enterprise Miner:
- Watch a video overview about creating a statistical model.
- Take a training course.
- Work through an end-to-end example of data mining.
- Watch an introductory webinar.
- Request access to the secure documentation site.In the subject field of the SAS Technical Support form, include SAS Enterprise Miner. Provide a site number that is associated with a current license for SAS Enterprise Miner. Only licensed SAS Enterprise Miner customers may receive access.
Free Online Documentation
Reference Help for SAS Enterprise Miner and SAS Enterprise Miner High-Performance Data Mining is provided in the product and on a secure site. The secure site requires a user ID and password, which you can obtain by contacting SAS Technical Support directly. In order to expedite your request, please include SAS Enterprise Miner documentation PDF or SAS Enterprise Miner High-Performance Data Mining documentation PDF in the subject field of the form.
To access any of the secure documentation listed below, see SAS Enterprise Miner Secure documentation
SAS Enterprise Miner 13.1
- Getting Started with SAS Enterprise Miner 13.1
PDF (2.1MB) | HTML
- Example Data for Getting Started with SAS Enterprise Miner 13.1 [ZIP]
- SAS Enterprise Miner 13.1: Administration and Configuration
PDF (2.8MB) | HTML
Developing Credit Scorecards Using Credit Scoring for SAS Enterprise Miner
PDF (1.7MB) | HTML
- Data Mining using SAS Enterprise Miner: A Case Study Approach, Third Edition
PDF (3.4MB) | HTML
- SAS Enterprise Miner 6, 7, 12, and 13: C and Java Score Code Basics [PDF] (1.3MB)
- SAS Enterprise Miner 13.1 Extension Nodes Developer's Guide
PDF (8.8MB) | HTML
- SAS Enterprise Miner 13.1: Reference Help (Secure Document)
- Help for SAS Enterprise Miner 13.1 is accessible within the product
SAS Enterprise Miner 13.1 High-Performance Data Mining
- SAS Enterprise Miner 13.1: High-Performance Procedures (Secure Document)
- Running SAS Enterprise Miner 13.1 High-Performance Procedures in Alongside Mode Using SASHDAT Data (Secure Document)
- Base SAS 9.4 Procedures Guide: High-Performance Procedures, Second Edition
PDF (2.7MB) | HTML
All online documentation for supported releases of SAS Enterprise Miner [HTML]
- SAS Enterprise Miner titles in online bookstore [Buy]
- Featured Titles
- SAS Technical Papers for SAS Enterprise Miner [HTML]
SAS Publishing Representatives are available in the U.S. from 8-5 ET to answer your documentation questions. Contact us at 1-800-727-3228 or e-mail.
Curriculum consultants are available in the U.S. from 9-5 EST. Contact us at 1-800-333-7660 or e-mail.
International customers, please contact your country office.
Online Support ResourcesThis page contains online support resources that are specific to this product. Visit the Support page to access various self-help and assisted-help resources or submit a problem through the SAS Technical Support form.
DownloadsHot Fixes by Release
Air date: February 28, 2014
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Air date: December 5, 2013
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Air date: September 19, 2013
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Air date: August 30, 2013
- Replacing unwanted numeric values using the HP Transform node
- Rev Up Your RPM's: A Modeling Sampler, Part 1
- Rev Up Your RPM's: A Modeling Sampler, Part 2
- Rev Up Your RPM's: A Modeling Sampler, Part 3
- Profiling Segments
- Profiling a Target Variable Before a Predictive Model
- Imputing Missing Values
- Customer Segmentation Using Enterprise Miner
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