基于神经网络的有效数据挖掘与分类

Gaurab Tewary
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引用次数: 11

摘要

随着数据库的发展,数据库中存储的数据量迅速增加,大量的数据中隐藏着许多重要的信息。如果信息可以从数据库中提取出来,他们将为组织创造大量的利润。他们问的问题是如何提取这个值。答案是数据挖掘。数据挖掘从业者可以使用许多技术,包括人工神经网络、遗传学、模糊逻辑和决策树。尽管神经网络已经在许多情况下证明了自己,但由于其黑箱性质,许多从业者对神经网络持谨慎态度。本文概述了人工神经网络,并对其作为数据挖掘从业者首选工具的地位提出了质疑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effective Data Mining for Proper Mining Classification Using Neural Networks
With the development of database, the data volume stored in database increases rapidly and in the large amounts of data much important information is hidden. If the information can be extracted from the database they will create a lot of profit for the organization. The question they are asking is how to extract this value. The answer is data mining. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are wary of Neural Networks due to their black box nature, even though they have proven themselves in many situations. This paper is an overview of artificial neural networks and questions their position as a preferred tool by data mining practitioners.
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