Unearthing valuable insight – profitable data mining results with less time and effort
SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models 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, minimize credit risk, anticipate resource demands, increase response rates for marketing campaigns and curb customer attrition.

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Benefits

• Support the entire data mining process with a broad set of tools. Regardless of your data mining preference or skill level, SAS provides flexible software that addresses complex problems. Going from raw data to accurate, business-driven data mining models becomes a seamless process, enabling the statistical modeling group, business managers and the IT department to collaborate more efficiently.
• Build more models faster with an easy-to-use GUI. SAS Enterprise Miner’s process flow diagram environment dramatically shortens model development time for both business analysts and statisticians. SAS Enterprise Miner 6.1 includes an intuitive user interface that incorporates common design principles established for SAS software, as well as additional navigation tools. The process flow diagram provides a complete audit trail of the analyses, and along with user-defined notes and model result packages, is useful for version control. The GUI can be tailored for all analysts’ needs via flexible, interactive property sheets, code editors and display settings.
• Enhance accuracy of predictions and easily share reliable information to improve the quality of decisions. Better-performing models with new innovative algorithms enhance the stability and accuracy of predictions, which can be verified easily by visual model assessment and validation metrics. Both analytical and business users enjoy a common, easy-to-interpret visual view of the data mining process. Predictive results and assessment statistics from models built with different approaches can be displayed side-by-side for easy comparison. The resulting diagrams serve as self-documenting templates that can be updated easily or applied to new problems without starting over.
• Ease the model deployment and scoring process. Scoring – the process of applying a model to new data – is the end result of many data mining endeavors. SAS Enterprise Miner automates the tedious scoring process and supplies complete scoring code for all stages of model development in SAS, C, Java and PMML. The scoring code can be deployed in a variety of real-time or batch environments within SAS, on the Web or directly in relational databases. The outcome is faster implementation of data mining results.

Features

Powerful, easy-to-use GUI, as well as batch processing for large jobs

• Interactive GUI for building process flow diagrams.
• Batch processing code or scheduling large modeling and scoring jobs.

Scalable processing

• Java client/SAS server architecture scales from single-user to large enterprise solutions.
• Server-based processing and storage.
• Asynchronous model building.
• Ability to stop processing cleanly.
• Grid computing.
• Parallel processing.
• Multithreaded predictive algorithms.

Advanced predictive and descriptive modeling

• Clustering and self-organizing maps.
• Market basket analysis.
• Sequence and Web path analysis.
• Dimension reduction techniques.
• 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.

Business-based model comparisons, reporting and management

• Assessment features for comparing multiple models using lift curves, statistical diagnostics and ROI metrics.
• Highly visual model comparision 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.