文本分类是文本数据挖掘的重要技术。
Text categorization is one of the important techniques in textual data mining.
该文提出了一种粗糙谱聚类算法，并将其应用于文本数据挖掘。
This paper proposes a rough spectral clustering algorithm and apply the algorithm on text data mining.
挖掘的理论和应用研究是数据挖掘领域一个新的重要分支，介绍了一种文本数据挖掘方法。
Study and application of text data mining is one of the most important problems in the data mining. In this paper, we firstly study a method of text data mining.
文本数据挖掘也不同于常规意义上的数据挖掘，常规数据挖掘是在数据库中发现感兴趣的模式，而文本数据挖掘是从自然语言文本中发现模式。
The difference between regular data mining and text mining is that in text mining the patterns are extracted from natural language text rather than from structured databases of facts.
随着文本数据的迅速增长，文本挖掘已经成为了数据挖掘领域的一个重要的研究方向。
With the rapidly development of the text data, text mining have been an important study direction in data mining area.
提出广义数据空间的概念，使得数据挖掘能够在数据库或数据仓库的不同部位或不同的抽象级别上，对数字数据或者文本数据进行挖掘，加强了决策分析的功能和灵活性。
A concept of broad sense data space was proposed so that DM can be made in different part and different abstract hierarchy in database and data warehouse, which enhances the functions and flexibility.
文本数据分类以文本挖掘技术为基础与核心，是近年来数据挖掘和网络挖掘领域当中的一个研究热点。
Based on web mining technology, automatic text classification has become a hot research area in the field of data mining and net mining.
文本数据分类以文本挖掘技术为基础与核心，是近年来数据挖掘和网络挖掘领域当中的一个研究热点。
Based on web mining technology, automatic text classification has become a hot research area in the field of data mining and net mining.
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