The most recent release is SAS Enterprise Miner 15.2.
What's New
Java Web Start is not supported for SAS Enterprise Miner 15.2. A new client download page can be used in place of Java Web Start.
SAS Enterprise Miner Key Features
Intuitive interfaces
- Interactive GUI for building process flow diagrams
- Batch processing code for scheduling large modeling and scoring jobs
Data preparation, summarization, and exploration
- Access to and integration of structured and unstructured data sources
- Outlier filtering
- Data sampling and partitioning
- File import from various sources
- Tools for merging and appending
- Easy-to-use Graph wizard and Graph Explore node
- Segment profile plots
- Data transformations
- Interactive variable binning
- Data replacement
- Time series data preparation
- Rules Builder node for creating ad hoc data-driven rules and policies
- Market basket analysis
- Sequence and web path analysis
- Link analysis
Dimension-reduction techniques
- Unsupervised and supervised variable selection
- LAR (least angle regression) variable selection\
- Variable clustering
- Principal component analysis
- Time series dimension reduction
Predictive modeling and machine learning
- Clustering
- Self-organizing maps
- Linear and logistic regression
- Decision trees
- Gradient boosting
- Neural networks
- Partial least squares regression
- Two-stage modeling
- Memory-based reasoning
- Model ensembles, including bagging and boosting
- Time series data mining
- Survival analysis
- Incremental response (net lift) models
Open-source integration node enables you to:
- Write code in the R language inside SAS Enterprise Miner
- Use SAS Enterprise Miner data and metadata in your R code and return R results to SAS Enterprise Miner
- Perform model comparison and generate SAS score code for supported models
Select set of high-performance nodes
- HP Data Partition
- HP Explore
- HP Transform
- HP Impute
- HP Variable Selection
- HP Principal Components
- HP Cluster
- HP BN Classifier
- HP Regression
- HP Neural
- HP Forest
- HP Tree
- HP GLM
- HP SVM
Fast, easy, and self-sufficient way for business users to generate models
- SAS® Rapid Predictive Modeler automatically generates predictive models for a variety of business problems and produces concise reports (including variable importance charts, lift charts, ROC charts, and model scorecards) for easy consumption and review
- Business analysts and subject-matter experts work from SAS® Enterprise Guide® or SAS® Add-In for Microsoft Office (Excel only)
- Models can be opened, augmented, and modified in SAS Enterprise Miner
Model comparisons, reporting, and management
- Assessment features for comparing multiple models by using lift curves, statistical diagnostics, and ROI metrics
- The innovative Cutoff node to determine probability cutoff points for binary targets
- Report creation and distribution
- Group processing for multiple targets and segments
- Interactive environment for comparing competing models and assessing the importance of key input variables on the predicted response times
- Integrated environment provided by the Register Model node for registering models into the SAS® Metadata Server
- Models that are developed with SAS code can also be registered into the SAS Metadata Server by using a SAS macro
Automated scoring process
- Interactive scoring in a variety of real-time or batch environments
- Automatic generation of score code in SAS, C, Java, and PMML
- Creation of the code needed to score models directly inside Aster, EMC Pivotal (previously Greenplum), IBM DB2, IBM Netezza, Oracle, and Teradata databases with SAS® Scoring Accelerator
- Integration of SAS Enterprise Miner training and scoring processes directly into other SAS solutions
Open, extensible design
- Extension nodes for easily adding tools and personalized SAS code
- Interactive editor features for training and score code in the SAS Code node
- Integration of text mining for analysis of both structured and unstructured data
- Incorporation of time series, web paths, and association rules as additional input variables into the model development process
Scalable processing
- Both the Java client and the SAS server architecture scale from single-user solutions to large-enterprise solutions
- Server-based processing and storage
- Grid computing, in-database, and in-memory processing options
- Asynchronous model building
- Ability to stop processing cleanly
- Parallel processing
Get Started
Ready to create models from your data? These resources are a good place to start.
Watch video
Learn more about using the sample, explore, modify, model and assess steps in SAS Enterprise Miner.
Learn the basics
Get up-to-speed on SAS Enterprise Miner features and functions with these tutorials:
Stay connected
Be a part of the community for SAS Enterprise Miner. We offer a variety of ways for you to interact with users and experts.
Tutorials
Browse our library of free SAS Enterprise Miner tutorials to learn something new or sharpen your skills.
Learn how to use the open source integration node to run R code in SAS Enterprise Miner to build models.
Sample Data
Modify Input Data
Assess Model Performance
Advanced Tutorials
Expand your SAS Enterprise Miner skill set with next-level tutorials.
Tune Random Forests and Support Vector Machines (video, 20:36)
Learn how to tune your random forest and support vector machine algorithms to train high quality models.
Training Courses
Learn SAS technology quickly and efficiently by taking a course from the analytics experts.
Books
For more in-depth information, read books written by SAS experts.
SAS Technical Papers
Read technical papers to learn how to complete a task or achieve a goal.
Applications
- Feature Extraction Methods for Time Series Data in SAS Enterprise Miner
- Incremental Response Modeling Using SAS Enterprise Miner
Association, Sequence & Link Analysis
Credit Scoring
- Building Loss Given Default Scorecard Using Weight of Evidence Bins in SAS Enterprise Miner
- Creating Interval Target Scorecards with Credit Scoring for SAS Enterprise Miner
Data Exploration & Visualization
High-Performance Data Mining
Predictive Modeling & Machine Learning
- Best Practices for Machine Learning Applications
- Clustering Techniques to Uncover Relative Pricing Opportunities: Relative Pricing Corridors Using SAS Enterprise Miner and SAS Visual Analytics
- Ensemble Modeling: Recent Advances and Applications
- Leveraging Ensemble Models in SAS Enterprise Miner
- An Overview of Machine Learning with SAS Enterprise Miner
- SAS Does Data Science: How to Succeed in a Data Science Competition
Text Mining
- 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
- Getting More from the Singular Value Decomposition (SVD): Enhance Your Models with Document, Sentence, and Term Representations
- Temporal Text Mining: A Thematic Exploration of Don Quixote
- Using Boolean Rule Extraction for Taxonomic Text Categorization for Big Data
Utilities
Software Downloads
The downloads application contains updates for SAS products and solutions.
SAS Enterprise Miner Blogs & Communities
Connect with other SAS users by joining a users group or attending an upcoming event.