A Survey on Text Mining Techniques

Sayali Sunil Tandel, A. Jamadar, Siddharth Dudugu
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引用次数: 5

Abstract

As there is fast growth in digital data collection techniques it has made way for large amount of data. Greater than 85% of present day data is comprised of unsaturated and unstructured data. Determining the definite patterns and trends to examine a textual data is biggest issue in text mining The various domains associated together in data mining are text mining, web mining, graph mining, and sequencing mining. The selection of proper and correct technique of text mining enhances the hustle and by lowering the period and struggle done to mine important information. Here, we talk about text data mining, various techniques of text data mining and also application of text data mining. Text data mining is used for obtaining stimulating and fascinating designs from the unsaturated texts which are derived from various sources. It changes words, phrases and sentences of an unstructured information into mathematical value linking with the saturated information in the database and analyses it with traditional data mining techniques. Information extraction, information retrieval, summarization, categorization and clustering are the different techniques of text mining.
文本挖掘技术综述
随着数字数据收集技术的快速发展,它为大量数据让路。目前超过85%的数据是由不饱和和非结构化数据组成的。确定确定的模式和趋势来检查文本数据是文本挖掘中最大的问题。数据挖掘中相关的各个领域有文本挖掘、web挖掘、图挖掘和序列挖掘。选择合适、正确的文本挖掘技术,可以降低重要信息挖掘的周期和工作量,提高挖掘效率。本文主要讨论了文本数据挖掘、文本数据挖掘的各种技术以及文本数据挖掘的应用。文本数据挖掘用于从各种来源的不饱和文本中获得令人兴奋和吸引人的设计。它将非结构化信息中的词、短语、句子等转化为与数据库中饱和信息相关联的数学值,并用传统的数据挖掘技术对其进行分析。信息抽取、信息检索、摘要、分类和聚类是文本挖掘的不同技术。
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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