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SAS Enterprise Miner

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, anticipate resource demands, increase acquisitions and curb customer attrition.
以对企业大量数据的分析为基础,SAS Enterprise Miner简化了数据挖掘过程并建立了高度精确的生产和描述性模型。 Forward-thinking组织今天使用SAS数据挖掘软件查出欺骗，期望资源要求、增量承购和遏制顾客损耗。

Benefits
Support the entire data mining process with a broad set of tools.
Build more models faster with an easy-to-use GUI.
Enhance accuracy of predictions and easily surface reliable business information.
Ease the model deployment and scoring process.
优点：
用一套完善的工具支持整个数据挖掘过程。
用一个简单易用的 GUI 更快地建立更多的模型。
提高预测和显示准确商业信息的准确性。
舒缓模型部署和评分过程。

Features
Multiple interfaces
Scalable processing
Data preparation, summarization and exploration
Advanced predictive and descriptive modeling
Business-based model comparisons, reporting and management
Automated scoring process
Open, extensible design
特征：
多个接口
可扩展工序
数据整理,汇总和探究
先进的预测和描述模型
商业基础模型比较,报告和管理
自动评分过程
可扩展设计

“Our profitability around marketing interventions programs is much higher because of the precision of understanding that SAS provides.”
—David Norton
Senior Vice President of Relationship Marketing
“ 由于SAS提供精确的支持,我们对周围市场进行干预从而获益的能力更高。”
— 大卫 Norton
营销关系部高级副总裁

Screenshots
SAS Enterprise Miner software's easy-to-use GUI for data mining.
数据挖掘的屏幕快照
SAS Enterprise Miner 是微软的简易数据挖掘程序GUI
How SAS® Is Different
Data access, management and cleansing are seamlessly integrated, making it easier to prepare data for analysis.
Robust variable selection and data modification tools improve the quality of your data, which leads to better modeling and more reliable results.
With multithreaded algorithms and support for multiprocessing and grid computing, execution time is reduced and hardware resources are used more efficiently.
Smart defaults allow business users to produce models quickly, while advanced statisticians can tweak details and embed their own algorithms into their model flows.
The rich Java client interface enables fast, maintenance-free distribution throughout large organizations, and data mining projects can be shared among analysts across different units and regions.
Our advanced analytic algorithms are organized under the core tasks that are performed in any successful data mining endeavor: Sampling, Exploration, Modification, Modeling and Assessment (SEMMA). You are guided through each step as the data mining project develops.
Unlike other data mining solutions that limit you to a single algorithm, we provide multiple advanced predictive and descriptive modeling algorithms, including market basket analysis, decision trees, gradient boosting, neural networks, linear and logistic regression, and many more.
Scoring code is delivered in SAS, C, Java and PMML for scoring in batch and real-time in both SAS and non-SAS environments.
SAS ® 是如何与众不同的？
数据获得、管理和清理零距离互动使得整理数据用于分析更加简单。
Robust变量选择和数据修改工具提高了数据的质量，它指向了更好的模型和更令人信服的结果。
多线程的算法和多处理和网格计算的支持，减少了执行时间，并更有效地使用硬件资源。
智能默认值允许商业用户更快地生成模型，而先进的统计学家可以调整详细信息，将自己的算法嵌入到他们的模型流中。
丰富的 Java 客户端界面允许快速、启用免维护的分布，在大型组织，整个数据挖掘项目可以跨越不同的单位和地区在分析人员之间共享。
我们先进的分析算法是在数据挖掘核心下组织的，这一核心体现在每一次数据挖掘的努力中： 取样、 探索、 修改、 建模和评估 (SEMMA)。 随着数据挖掘项目的发展，你将被一步一步地引导。
与其他数据解决方案限制您单一算法不同的是，我们提供了多个先进的预测和描述性建模算法包括市场篮分析、 决策树、 推进神经网络线性渐变和逻辑回归和更多。
评分码在 SAS、 C、 Java 和 PMML中已传递出，而且支持 SAS 与非 SAS 环境中的实时评分代码。