Text clustering is one of the important methods.
文本聚类是处理文本的重要方法之一。
Spectral clustering is a new method for text clustering.
谱聚类是文本聚类分析较常用的一种新型方法。
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。
Text clustering is an important technology for Topic Detection and Tracking.
文档聚类是实现话题检测与跟踪的重要方法。
To implement high efficient complex searching, one solution is text clustering.
为了实现高效的复杂查询，一种解决方案是实现文档的聚类。
This paper provides the design and realization of a text clustering prototype system.
本文提出了一种文本聚类系统原型的设计与实现。
As an exploratory data analysis method, text clustering is very important in text mining.
文本聚类是目前文本挖掘中重要的探索性数据分析方法。
Study on Chinese text clustering models in compliance with the characteristics of Chinese texts.
针对中文文本组成上的特点，研究了中文文本聚类的模型。
Thus, it has become an increasingly important task to find an effective text clustering method.
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。
Most clustering algorithms can not meet the demand of speed and self-adapting about text clustering.
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。
But the research of Chinese text clustering is at its early stage, and there are still many problems to be resolved.
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。
Experiment results indicate that the proposed algorithm outperforms the existing text clustering algorithms in accuracy.
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。
Currently, common text clustering methods are based on document content, in which global document information is needed.
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。
This paper proposed a new model of dynamic fuzzy Kohonen neural network (TGFCM), which was applied to the text clustering.
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 presents a new method of text clustering by using the latent semantic index (LSI) and self-organizing neural network (SNN).
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。
Inspired by the nonnegative matrix factorization algorithm, we put forward an fuzzy text clustering method based on nonnegative factor analysis.
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。
Text clustering is an important research branch of clustering method and it is the application of clustering method used in text processing field.
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。
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。
The combination of the ant clustering technology and the text clustering technology leads to the development of ant-based text clustering algorithms.
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 method of automatic abstracting based on text clustering was brought forward to overcome the shortages of the current methods of automatic abstracting.
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。
This paper discusses different Vector Space Model(VSM)-based clustering algorithms and presents an improved text clustering algorithm——Level-Panel(LP)algorithm.
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。
We propose a method, use Unsupervised text Clustering algorithms (UTC) to guid text classification, so as to deal with text classification without training set.
提出了一种用无监督聚类算法指导文本分类的方法，以解决没有训练集的文本分类问题。
Using HowNet's complete knowledge system to construct Concept Dictionary and Concept Hierarchy, we realized a kind of Chinese text clustering algorithm based on concept.
利用知网较完备的知识体系来构造概念词典和概念层次结构，实现了一种以知网为背景知识的基于概念的中文文本聚类算法。
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.
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。
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.
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。
Based on concept space and text clustering technique as well as traditional keyword searching method, it could help users to locate the information they need quickly and precisely.
这种检索方法在文本聚类的基础上，基于概念空间并与传统的关键词检索相结合能够帮助用户快速、准确地定位所需要查找的信息。
The clustering method USES curve-fitting to implement the text clustering by auto threshold-detection means, and complete the whole clustering process through result revising phase.
该聚类算法通过曲线拟合技术来实现文本的自动阈值确定和聚类划分，并最终通过聚类间的迭代和结果修正来完成整个聚类过程。
Since Chinese network short text is less of keywords and full of anomalous writings, the traditional text clustering method is not directly suitable for network short text clustering.
然而，中文网络短文本固有的关键词词频低、存在大量变形词等特点，使得难以直接使用现有面向长文本的聚类算法。
Through analyzing about traditional clustering methods, we present the text clustering based on the community detecting algorithms, adopt it to cluster the text datas and have good effect.
通过对传统文本聚类的实现与分析，将复杂网络中的社区划分算法应用文本聚类中，实现基于社区划分算法的文本聚类，并取得一定的效果。
If clustering text, you need to convert the text to a numeric representation.
如果创建文本集群，您需要将文本转换成数值表示。
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