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For exchanging the extracted models – in particular for use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.

For exchanging the extracted models – in particular for use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.

为了交换所提取的模型，特别是在预测分析中使用，关键的标准是预测模型标记语言 PMML，这是一种基于 xml 的语言，由数据挖掘集团 DMG 开发，并支持作为交换格式的许多数据挖掘应用程序。顾名思义，它只涵盖预测模型，这是一项对业务应用程序非常重要的特殊数据挖掘任务。然而，覆盖子空间聚类的扩展(例如)已经独立于 DMG 被提出。

为了交换所提取的模型，特别是在预测分析中使用，关键的标准是预测模型标记语言 PMML，这是一种基于 XML 的语言，由数据挖掘集团 DMG 开发，并支持作为交换格式的许多数据挖掘应用程序。顾名思义，它只涵盖预测模型，这是一项对业务应用程序非常重要的特殊数据挖掘任务。然而，覆盖子空间聚类的扩展已经独立于 DMG 被提出。

==显著用途 Notable uses==

==显著用途 Notable uses==