SAS® Enterprise Miner Features

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.

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