Data mining

C. Olaru, L. Wehenkel
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引用次数: 46

Abstract

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.
数据挖掘
数据挖掘(DM)是一项复杂活动的民间名称,旨在从大型数据库中提取合成的和以前未知的信息。它还表示研究和开发算法和软件环境的多学科领域,以支持在经常有大量数据可供挖掘的现实问题背景下的这种活动。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,有时DM被认为只是称为数据库中的知识发现(KDD)的更广泛的整体过程中的一个步骤,或者是后者的同义词。本教程介绍了数据挖掘的概念,旨在帮助理解整个过程和所涉及的工具:过程是如何产生的,可以用它做什么,它背后的主要技术是什么,以及哪些是可操作的方面。本教程还介绍了一些数据挖掘的应用实例,从而激励电力系统领域成为一个非常合适的数据挖掘应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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