文本挖掘技术与方法综述

Shivaprasad Km, T. H. Reddy
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引用次数: 0

摘要

在过去的几十年里,我们见证了数据的巨大积累和使用。这些数据面临的主要问题是不匹配和过载。不匹配是一些有用或有趣的数据被忽略了,过载只是收集的数据不是用户需要的数据。为了克服这一问题,人们开发了一种文本挖掘技术。文本挖掘从大量的非结构化数据中提取有用的、有趣的数据;这有助于处理问题。文本挖掘中的一项复杂任务是对提取的数据进行分析和分类。为了高效、有效地提取和分析数据的模式,近年来发展了分类、聚类、摘要、词干提取等各种技术和方法。本文对其中的一些技术和方法进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on Text Mining Techniques and Methods: A Review Approach
Over last few decades, we have witnessed the enormous accumulation and usage of the data. Major issues faced by this data are mismatch and overload. The mismatch is the some useful or interesting data has been overlooked and overload is nothing but the gathered data is not one the user needed. To overcome this issue a technique of text mining has been developed. Text mining extracts the useful and interesting data from the large unstructured data; it helps to cope up with the issues. A complex task in text mining is the analysis and categorization of the extracted data. For the efficient and effective extraction and analysis of the patterns of data, various techniques and methods like categorization, clustering, summarization, stemming etc. have been recently developed. Some of the techniques and methods are discussed in this paper.
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