文本聚类是处理文本的重要方法之一。
最后给出文本聚类结果描述的评价方法。
The evaluation methods of DCD are also described in this paper.
本文提出了一种文本聚类系统原型的设计。
This paper provides a model designment of text clustering system.
本文提出了一种文本聚类系统原型的设计与实现。
This paper provides the design and realization of a text clustering prototype system.
谱聚类是文本聚类分析较常用的一种新型方法。
文本分类和文本聚类是信息处理中的两个重要工作。
Text classification and cluster are two important missions of information processing.
论文的重点是在文本聚类指导下的分类模式的提取。
The important part of the thesis is the extraction of clustering guided classification model.
文本聚类是目前文本挖掘中重要的探索性数据分析方法。
As an exploratory data analysis method, text clustering is very important in text mining.
针对中文文本组成上的特点，研究了中文文本聚类的模型。
Study on Chinese text clustering models in compliance with the characteristics of Chinese texts.
目前多数聚类算法不能很好地适应文本聚类的快速自适应需求。
Most clustering algorithms can not meet the demand of speed and self-adapting about text clustering.
提出一种基于球形的模糊c -均值算法的中文文本聚类方法。
A clustering algorithm for Chinese documents based on the spherical fuzzy c-means algorithm is presented.
实验表明，该算法与现有的文本聚类算法相比，准确率有一定的提高。
Experiment results indicate that the proposed algorithm outperforms the existing text clustering algorithms in accuracy.
因此，寻找一种行之有效的文本聚类算法已成为一个重要的研究课题。
Thus, it has become an increasingly important task to find an effective text clustering method.
蚁群聚类算法与文本聚类技术的结合就形成了基于蚁群的文本聚类算法。
The combination of the ant clustering technology and the text clustering technology leads to the development of ant-based text clustering algorithms.
但是国内中文文本聚类的研究还处于初期阶段，还存在许多问题亟待解决。
But the research of Chinese text clustering is at its early stage, and there are still many problems to be resolved.
针对当前自动文摘方法的不足，提出了基于文本聚类的自动文摘实现方法。
The method of automatic abstracting based on text clustering was brought forward to overcome the shortages of the current methods of automatic abstracting.
目前，常见的文本聚类都是基于文档内容的，通常需要获得全局的文档信息。
Currently, common text clustering methods are based on document content, in which global document information is needed.
本文借助于非负矩阵分解算法，提出了一种基于非负因子分析的模糊文本聚类方法。
Inspired by the nonnegative matrix factorization algorithm, we put forward an fuzzy text clustering method based on nonnegative factor analysis.
提出了一种新的动态模糊自组织神经网络模型(TGFCM)，并将其用于文本聚类中。
This paper proposed a new model of dynamic fuzzy Kohonen neural network (TGFCM), which was applied to the text clustering.
文本聚类是聚类分析领域的一个重要研究分支，是聚类方法在文本处理领域的重要应用。
Text clustering is an important research branch of clustering method and it is the application of clustering method used in text processing field.
传统的中文文本聚类方法需要对高维向量进行处理，有对中文文本需要进行分词处理等困难。
Traditional method faces the difficulties that need to handle high dimension vector and Chinese word segment.
文本聚类在信息过滤，网页分类中有着很好的应用。但它面临数据量大，特征维度高的难点。
Document clustering had been employed in information filtering, web page classification and so on.
根据隐含语义索引（LSI）理论和动态自组织映射神经网络理论，提出了一种文本聚类的新方法。
This paper presents a new method of text clustering by using the latent semantic index (LSI) and self-organizing neural network (SNN).
文本表示中特征项的权值计算方法决定了文本特征的提取，在很大程度上影响了文本聚类的准确率。
Computing method of weighted value for feature item based on text representation can determine extraction of text feature, which have influence on accuracy of the text clustering.
该文探讨了基于向量空间模型的文本聚类方法，提出 了一种文本聚类的改进算法——LP 算法。
This paper discusses different Vector Space Model(VSM)-based clustering algorithms and presents an improved text clustering algorithm——Level-Panel(LP)algorithm.
针对词、潜在概念、文本和主题之间的模糊关系，提出一种基于信息论的潜在概念获取与文本聚类方法。
To emphasize the fuzzy relation among words, latent concepts, text and topics, an information theory based approach to latent concept extraction and text clustering is proposed.
本文对文本聚类问题的文本聚类算法进行了深入的讨论和研究，设计并实现了基于新算法的中文文本聚类系统。
The system of Chinese texts clustering based on new algorithm is implemented in this paper after discussion and research on format of texts vector and texts clustering algorithms.
文本聚类，即将给定的文本集合划分为多个簇，从而达到簇内文本的主题相关性，簇间文本的主题无关性的目的。
Document clustering is to separate the document set into groups, in each group the documents are of the same or related topic.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
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