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