SAS Enterprise Miner Features List
Intuitive interfaces
- Interactive GUI for building process flow diagrams.
- Batch processing code for scheduling large modeling and scoring jobs.
Data preparation, summarization & exploration
- Access and integrate structured and unstructured data sources.
- Outlier filtering.
- Data sampling.
- Data partitioning.
- File import.
- Merge and append tools.
- Univariate statistics and plots.
- Bivariate statistics and plots.
- Batch and interactive plots.
- Segment profile plots.
- Easy-to-use Graphics Explorer wizard and Graph Explore node.
- Interactively linked plots and tables.
- Data transformations.
- Time series data preparation and analysis.
- Interactive variable binning.
- Rules Builder node for creating ad hoc data-driven rules and policies.
- Data replacement.
Advanced predictive & descriptive modeling
- Clustering and self-organizing maps.
- Market basket analysis.
- Sequence and web path analysis.
- Link analysis.
- Dimension reduction techniques:
- Variable selection.
- LARS (Least Angle Regression) variable selection.
- Principal components.
- Variable clustering.
- Time series mining.
- 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.
- Ratemaking for insurance.
- Incremental response/net lift models.
Open source R integration node
- Write code in the R language inside of SAS Enterprise Miner.
- Makes SAS Enterprise Miner data and metadata available to your R code and returns R results to SAS Enterprise Miner.
- Training and scoring for supervised and unsupervised R models.
- Allows for data transformation and data explorations of R models in SAS Enterprise Miner.
- Generates model comparisons and SAS score code for supported models.
Select set of high-performance procedures & nodes
- Multithreaded, high-performance procedures:
- High-performance variable reduction.
- High-performance neural networks.
- High-performance random forests.
- High-performance 4score.
- High-performance decide.
- High-performance data mining database.
- High-performance sampling.
- High-performance data summarization.
- High-performance imputation.
- High-performance binning.
- High-performance correlation.
- High-performance Bayesian network.
- High-performance clustering.
- High-performance Support Vector Machine.
- Multithreaded, high-performance nodes:
- HP Data Partition.
- HP Explore.
- HP Transform.
- HP Variable Selection.
- HP Regression.
- HP Neural.
- HP Forest.
- HP Impute.
- HP Tree.
- HP GLM.
- HP Principal Components.
- HP Cluster.
- HP SVM.
Fast, easy & self-sufficient way for business users to generate models
- SAS Rapid Predictive Modeler automatically generates predictive models for a variety of business problems.
- Business analysts and subject-matter experts work from SAS Enterprise Guide or the SAS Add-In for Microsoft Office (Excel only).
- Models can be opened, augmented and modified in SAS Enterprise Miner.
- Produces concise reports, including variable importance charts, lift charts, ROC charts and model scorecards, for easy consumption and review.
- Ability to score the training data with an option to save the scored data set.
Model comparisons, reporting & management
- Assessment features for comparing multiple models using lift curves, statistical diagnostics and ROI metrics.
- Highly visual model comparison interface.
- Innovative Cutoff node examines to determine probability cutoff point(s) for binary targets.
- Report creation and distribution.
- Model result packages.
- Group processing for multiple targets and segments.
- Interactive environment for comparing and contrasting competing models and assessing the importance of key input variables on the predicted response times.
- Register Model node provides integrated environment for model registration into the SAS Metadata Server.
- Macro can also be used for registering models developed with SAS code into the SAS Metadata Server.
Automated scoring process
- Interactive scoring in a variety of real-time or batch environments.
- Automatically generates score code in SAS, C, Java and PMML.
- Score data based on models saved as PMML documents (experimental).
- Score SAS Enterprise Miner models directly inside Aster, EMC Pivotal (previously Greenplum), IBM DB2, IBM Netezza, Oracle and Teradata databases with SAS Scoring Accelerator.
- Model registration and management.
- Deploy models in multiple environments.
- Integrate SAS Enterprise Miner training and scoring processes directly into other SAS solutions.
Open, extensible design
- Extension node for easily adding tools and personalized SAS code.
- Interactive editor features for training and score code.
- Integrate text mining for analysis of both structured and unstructured data.
- Incorporate time series, Web paths and associations rules as additional input variables into the model development process.
Scalable processing
- The Java client and the SAS server architecture both scale from single-user 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.
- Multithreaded predictive algorithms.