SAS® Enterprise Miner™ and SAS Factory Miner®
SAS Enterprise Miner streamlines the data mining process and creates highly accurate predictive and descriptive models that are based on analysis of vast amounts of data from across the enterprise. Forward-thinking organizations today are using SAS data mining software to detect fraud, anticipate resource demands, increase acquisitions, and curb customer attrition.
The suite of statistical, data mining, and machine learning algorithms in SAS Enterprise Miner helps data miners, statisticians, marketing analysts, database marketers, risk analysts, fraud investigators, engineers, scientists, and business analysts examine large amounts of data in order to identify and solve critical business or research issues.
SAS Enterprise Miner offers state-of-the-art predictive analytics and data mining capabilities that enable users to analyze complex data, discover unknown patterns and key relationships, generate forecasts, and build models efficiently through an easy-to-use, drag-and-drop interface. Here is a list of key features.
SAS Factory Miner, which runs as an add-on to SAS Enterprise Miner, provides an automated web-based framework to build and retrain predictive models across business segments (region, product, mobile phone plan, and so on) or customer segments (such as married versus single versus family customers, or high-value versus low-value customers). It enables you to use the latest machine learning and statistical algorithms to test many modeling approaches simultaneously across many segments. You can assess performance of the entire model portfolio and identify underperforming models for manual fine-tuning. SAS Factory Miner shortens model development time, enabling you to make timely decisions and be much more productive, efficient and focused on deriving valuable outcomes from granular segments. Here is a list of key features.
The most current releases are SAS Enterprise Miner 14.1 and SAS Factory Miner 14.1.
SAS Enterprise Miner 14.1
SAS Enterprise Miner 14.1 provides the following improvements and enhancements:
- The PMML score code generated by SAS Enterprise Miner 14.1 is now PMML version 4.2.
- The Incremental Response node features a new Prescreening Variable property. This property enables you to specify whether node variable selection is performed using Net Information Value scores or Adjusted Net Information Value scores.
- SAS High-Performance Data Mining 14.1 provides the following new features and enhancements:
- The new HP Bayesian Network Classifier node fits a Bayesian network for class targets.
- The HP Variable Selection node adds a tree-based selection method and a Boolean screening property for use with the LAR/LASSO algorithm.
- The HP Cluster node enables you to request automatic selection of the number of clusters by using the ABC criterion.
- Each of the HP SVM and HP Forest nodes can create an analytic store, which is a portable format of the model that can be used to score observations within a database.
- The HP Forest node has several other enhancements:
- A new variable importance method for variable selection, called random branch assignments
- New default values for leaf size (1) and leaf fraction (0.00001) in order to improve model accuracy
- Improved data distribution when the HPFOREST procedure is run in distributed mode
- The HP Regression node provides a fast backward selection method for logistic regression.
- The HP Tree node provides the following new features:
- Options for the Nominal Target Criterion property are expanded to include Information Gain Ratio and CHAID.
- Options for the Interval Target Criterion property are also expanded to include CHAID.
- A new Surrogate Rules property adds an option for handling missing values in the data.
- A new Use Input Once property controls whether an input can be used multiple times or at most once in a branch.
- Several new subtree methods are included, such as cost-complexity pruning (which uses cross validation when no validation data are present) and pruning and subtree selection (which is based on minimizing an assessment measure such as misclassification rate or average square error).
SAS Factory Miner 14.1
SAS Factory Miner 14.1 provides the following key capabilities:
- Builds and retrains multiple predictive models, including Bayesian networks, decision trees, generalized linear models, neural networks, random forests, regression, and support vector machines across segments, such as state, region, or product
- Provides model templates that can be customized to include data preparation capabilities, variable selection, and various options for the predictive model that is used; these templates can then be shared across projects or users
- Identifies a champion model (based on one of several statistical criteria for each segment) that can easily be deployed in production environments, and generates complete scoring code for each model
Here are our top suggestions for new users of SAS Enterprise Miner:
- Watch a series of six Getting Started videos:
- Read our Getting Started documentation:
- Watch an introductory webinar.
- Take a training course.
Here are our top suggestions for new users of SAS Factory Miner:
- Watch a technical demo
Secure Documentation for SAS Enterprise Miner and SAS Factory Miner
Request access to the secure documentation site.
Free Online Documentation
SAS Enterprise Miner and SAS Enterprise Miner High-Performance Data Mining
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 14.1
- Getting Started with SAS Enterprise Miner 14.1 PDF | HTML
- Example Data for Getting Started with SAS Enterprise Miner 14.1 ZIP
- SAS Enterprise Miner 14.1: Administration and Configuration PDF | HTML
- Developing Credit Scorecards Using Credit Scoring for SAS Enterprise Miner PDF | HTML
- Data Mining using SAS Enterprise Miner: A Case Study Approach, Third Edition PDF | HTML
- SAS Enterprise Miner 6, 7, 12, 13, and 14: C and Java Score Code Basics PDF
- SAS Enterprise Miner 14.1 Extension Nodes Developer's Guide PDF | HTML
- SAS Enterprise Miner 14.1: Reference Help (Secure Document)
- Help for SAS Enterprise Miner 14.1 is accessible within the product
SAS Enterprise Miner 14.1 High-Performance Data Mining
- SAS Enterprise Miner 14.1: High-Performance Procedures (Secure Document)
- Base SAS 9.4 Procedures Guide: High-Performance Procedures, Fourth Edition PDF | HTML
SAS Factory Miner
The documentation for SAS Factory Miner is provided on a secure site that requires a user ID and password, which you can obtain by contacting your SAS consultant or SAS Technical Support. In order to expedite your request, please include SAS Factory Miner in the subject field of the form.
To access any of the secure documentation listed below, see SAS Factory Miner
SAS Factory Miner 14.1
- SAS Factory Miner 14.1: Administration and Configuration (Secure Document)
- SAS Factory Miner 14.1: User's Guide (Secure Document)
- Accessibility Features for SAS Factory Miner 14.1 (Secure Document)
All online documentation for supported releases of SAS Enterprise Miner [HTML]
- SAS Enterprise Miner titles in online bookstore [Buy]
- Featured Titles
- Feature Extraction Methods for Time Series Data in SAS Enterprise Miner
- Incremental Response Modeling Using SAS Enterprise Miner
Association, Sequence, and Link Analysis
- Creating Interval Target Scorecards with Credit Scoring for SAS Enterprise Miner
- Building Loss Given Default Scorecard Using Weight of Evidence Bins in SAS Enterprise Miner
Data Exploration and Visualization
High- Performance Data Mining
- A New Age of Data Mining in the High-Performance World
- Scalability of the SAS/STAT HPGENSELECT High-Performance Analytical Procedure: A Comparison with RevoScaleR
Predictive Modeling and Machine Learning
- SAS Does Data Science: How to Succeed in a Data Science Competition
- Clustering Techniques to Uncover Relative Pricing Opportunities: Relative Pricing Corridors Using SAS Enterprise Miner and SAS Visual Analytics
- Leveraging Ensemble Models in SAS Enterprise Miner
- An Overview of Machine Learning with SAS Enterprise Miner
- Using Boolean Rule Extraction for Taxonomic Text Categorization for Big Data
- Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining
- Automatic Detection of Section Membership for SAS Conference Paper Abstract Submissions: A Case Study
See all technical papers
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.
- Credential: SAS Certified Predictive Modeler using SAS Enterprise Miner 13
- Sample Questions
- SAS Academy for Data Science
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.
NotesSAS Enterprise Miner
Highlighted GitHub RepositoriesSAS-sponsored GitHub repositories include example process flow diagrams and accompanying materials to help you learn more about core data mining topics such as association analysis, clustering, credit scoring, predictive modeling, machine learning, survival analysis, and text mining. See the following:
dm-flowLibrary of SAS Enterprise Miner process flow diagrams to help you learn by example about specific data mining topics.
enlighten-applyExample code and materials that illustrate applications of SAS machine learning techniques.
enlighten-deepExample code and materials that illustrate using neural networks with several hidden layers in SAS.
enlighten-integrationExample code and materials that illustrate techniques for integrating SAS with popular open source analytics technologies like Python and R.
Air date: October 22, 2015
Air date: October 22, 2015
Air date: August 17, 2015
Air date: June 29, 2015
Air date: June 29, 2015
Air date: December 12, 2014
Air date: November 20, 2014
Air date: April 8, 2014
For more SAS Enterprise Miner videos, go to the SAS/STAT and SAS/OR Focus Area.
Getting Started Videos
- Setting Up an Enterprise Miner Project
- Exploring Input Data and Replacing Missing Values
- Building Decision Trees
- Imputing and Transforming Data, Building a Regression Model, and Building a Neural Network
- Comparing Models
- Scoring New Data
Machine Learning Webinar SeriesRegistration is required.
- Machine Learning: Principles and Practice
- Principle Component Analysis for Machine Learning
- Clustering for Machine Learning
- Ensemble Modeling for Machine Learning
- Deep Learning for Natural Language Processing
Training Tip Videos
- 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
- Rev up your RPM's: A Modeling Sampler, Part 4
- Profiling Segments
- Profiling a Target Variable Before a Predictive Model
- Imputing Missing Values
- Customer Segmentation Using Enterprise Miner
SAS Data Mining CommunityShare your experiences, questions and ideas with other data miners
Subconscious Musings BlogRead discussions about things related to the advanced analytics and data mining that solve many of the challenging problems facing business and organizations today
SAS Communities Library: Data MiningSee tips from SAS data mining developers and other experts