SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been helping customers around the world. SAS was developed at North Carolina State University from 1966 until 1976, when SAS Institute was incorporated. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data.
Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.
SAS offers a flexible data preparation and management capabilities to build models with a rich, interactive visualization and data exploration. SAS also uses in-memory, in-database and grid capabilities for faster response and results.It offers state-of-the-art predictive analytics and data mining capabilities that enable organizations to analyze complex data, find useful insights and act confidently to make fact-based decisions.
SAS Enterprise Miner is delivered as a distributed client/server system. This provides an optimized architecture so data miners and business analysts can work more quickly to create accurate predictive and descriptive models,and produce results that can be shared and incorporated into business processes. An easy-to-use, drag-and-drop interface is designed to appeal to analytic professionals.
The advanced analytic algorithms are organized under core tasks that are performed in any successful data mining endeavor. R language code can be integrated inside of a SAS Enterprise Miner process flow diagram. This enables you to perform data transformation and exploration as well as training and scoring supervised and unsupervised models in R.
Components of SAS Predictive Analytics and Data Mining
The components of SAS Predictive Analytics and Data Mining are exploratory data analysis to visually explore data sets,model development and deployment,high performance data mining, credit scoring, analytics acceleration,scoring acceleration,model management and monitoring.
SAS Enterprise Miner
Features: SAS Predictive Analytics and Data Mining
- Easy to use GUI
- Data preparation, summarization and exploration
- Advanced predictive and descriptive modeling
- Business friendly self sufficient way for business users to generate models
- Automated scoring process
- Build better models with a versatile data mining workbench.
- Univariate ,bivariate statistics and plots.
- Clustering and self-organizing maps.
- Market basket analysis.
- Linear and logistic regression.
- Decision trees.
- Gradient boosting & neural networks
- Support vector machine
- High-performance variable reduction.
- SAS add-n for Microsoft Office
- Grid computing, in-database and in-memory processing options.
SAS Enterprise Miner is deployable via a thin-client web portal for distribution to multiple users with minimal maintenance of the clients. A select set of high-performance data mining nodes is included in SAS Enterprise Miner. Depending on the data and complexity of analysis, users may find performance gains in a single-machine SMP mode.
SAS Enterprise Miner
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