文本挖掘技术综述

Yu Zhang, Mengdong Chen, Lianzhong Liu
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引用次数: 52

摘要

由于互联网上产生了大量的非结构化文本数据,文本挖掘被认为具有很高的商业价值。文本挖掘是从文本数据集合中提取以前未知的、可理解的、潜在的和实用的模式或知识的过程。本文介绍了文本挖掘的研究现状。然后介绍了几种通用模型,从整体上了解文本挖掘。最后根据应用情况将文本挖掘工作分为文本分类、文本聚类、关联规则提取和趋势分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A review on text mining
Because of large amounts of unstructured text data generated on the Internet, text mining is believed to have high commercial value. Text mining is the process of extracting previously unknown, understandable, potential and practical patterns or knowledge from the collection of text data. This paper introduces the research status of text mining. Then several general models are described to know text mining in the overall perspective. At last we classify text mining work as text categorization, text clustering, association rule extraction and trend analysis according to applications.
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