首页> 外文会议>12th Americas Conference on Information Systems(AMCIS 2006) vol.3 >Information Mining: Integrating Data Mining and Text Mining for Business Intelligence
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Information Mining: Integrating Data Mining and Text Mining for Business Intelligence

机译:信息挖掘:集成数据挖掘和文本挖掘以实现商业智能

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摘要

Data mining and text mining can help decision makers obtain business intelligence and make informed decisions, but using one of them gives us only a partial picture. The application of data mining can lead to questions that cannot be answered with only numbers. Therefore, decision makers will need text mining to drill the textual data to find explanations for numbers. On the other hand, the application of text mining will also raise questions that cannot be answered with only text. We need to examine and utilize findings from both. However, most of the current text mining applications and data mining applications are not integrated. In this paper, a framework for combining these two technologies is described. In this framework, a taxonomy complemented by feature indexing and full-text indexing will bridge data mining and text mining. The technical challenges of the integration are also discussed.
机译:数据挖掘和文本挖掘可以帮助决策者获得商业智能并做出明智的决策,但是使用其中之一仅能提供部分了解。数据挖掘的应用可能导致无法仅用数字回答的问题。因此,决策者将需要文本挖掘来钻取文本数据,以找到数字的解释。另一方面,文本挖掘的应用也会引发一些仅靠文本无法解决的问题。我们需要检查和利用两者的发现。但是,当前大多数文本挖掘应用程序和数据挖掘应用程序尚未集成。在本文中,描述了用于结合这两种技术的框架。在此框架中,由特征索引和全文索引补充的分类法将桥接数据挖掘和文本挖掘。还讨论了集成的技术挑战。

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