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Data mining

机译:数据挖掘

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摘要

Data mining (DM) is a folkloric denomination of a complex activity that aims at extracting synthesized and previously unknown information from large databases. It denotes also a multidisciplinary field of research and development of algorithms and software environments to support this activity in the context of real-life problems where often huge amounts of data are available for mining. There is a lot of publicity in this field and also different ways to see the things. Hence, depending on the viewpoints, DM is sometimes considered as just a step in a broader overall process called knowledge discovery in databases (KDD), or as a synonym of the latter. This tutorial presents the concept of data mining and aims at providing an understanding of the overall process and tools involved: how the process turns out, what can be done with it, what are the main techniques behind it, and which are the operational aspects. The tutorial also describes a few examples of data mining applications, so as to motivate the power system field as a very opportune data mining application.
机译:数据挖掘(DM)是一种复杂活动的民俗化名称,旨在从大型数据库中提取合成的和以前未知的信息。它还表示算法和软件环境研究和开发的多学科领域,以在经常有大量数据可用于挖掘的现实问题中支持此活动。在这个领域有很多宣传,也有不同的看待事物的方式。因此,根据不同的观点,DM有时仅被视为更广泛的整体过程中的一个步骤,称为数据库中的知识发现(KDD),或后者的同义词。本教程介绍了数据挖掘的概念,旨在提供对整个过程和所涉及工具的理解:该过程如何完成,可以完成什么,其背后的主要技术是什么以及操作方面。本教程还描述了数据挖掘应用程序的一些示例,以激励电力系统领域成为非常合适的数据挖掘应用程序。

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