All document clustering methods are based on keywords now.
现有的文献聚类方法都是通过文献关键词来进行的。
This paper presents a method to study interdisciplines by document clustering analysis.
本文提出了一种通过文献数据聚类分析来研究学科交叉的方法。
Document clustering had been employed in information filtering, web page classification and so on.
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。
Then use Fuzzy C-means to do document clustering based on the results of similarity calculation above.
然后采用模糊c均值根据上述计算文档相似度的结果对文档进行聚类。
Document clustering description is a problem of labeling the clustered results of document collection clustering.
标注文本集合聚类后生成的类簇被称为聚类描述问题。
The experiment results have shown that the hybrid clustering method can improve the document clustering performance.
实验结果表明，该聚类组合算法能改进文档聚类的性能。
Document clustering is to separate the document set into groups, in each group the documents are of the same or related topic.
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。
For new topic detection at the event level, method based on clustering was discussed mainly, which included the representation of documents, and document clustering.
对于事件层的新话题监测，主要阐述基于聚类的方法，包括文档的向量化表示以及文档聚类。
According to the requirement of online public opinion analysis, this paper builds an online public opinion hotspot detection and analysis system based on document clustering.
根据对网络舆情分析的需求，构建出基于聚类的网络舆情热点发现及分析系统。
In the LEI Administration database, in the Configuration document, we will see the Domino Clustering field enabled and other cluster-related fields properly configured by the installer (see figure 2).
在LEIAdministration数据库中，在Configuration文档中，将看到DominoClustering字段已被启用，其他与集群相关的字段已由安装程序适当地配置了(参见图2)。
This paper systematically studies and analyses the data mining technique, document mining and clustering analysis, and propose some improved algorithms.
本文对数据挖掘技术，尤其是文本挖掘和聚类分析进行了较为系统地分析和研究，提出了一些改进算法。
As an important unsupervised pattern recognition tool clustering analysis has been used in diverse fields such as data mining, biology, computer vision, document analysis.
聚类分析作为一种重要的非监督模式识别工具，可用于多种领域，如数据挖掘、生物学、计算机视觉、文档分析等。
After analyzing the disadvantages of the user profile based on-keywords vector in the existing document recommendation system, a novel representation of user profile based on clustering was proposed.
分析了现有文章推荐系统中基于关键词向量的用户模型表示方法存在的不足，提出了基于聚类兴趣点的用户模型表示方法。
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。
Secondly, the system can distinguish the domain of the web page and understand the document at the concept level by text classification, clustering and concept extraction based machine learning.
其次，采用机器学习技术，包括文本分类、聚类，文本概念抽取，从概念层次理解文本信息；
Clustering analysis is an important research in data mining, and has been widely used in many fields, such as message filtering, document categorization, bioinformatics, etc.
聚类分析是数据挖掘中重要的研究课题，在信息过滤、资料自动分类、生物信息学等领域得到广泛应用。
Clustering analysis is an important research in data mining, and has been widely used in many fields, such as message filtering, document categorization, bioinformatics, etc.
聚类分析是数据挖掘中重要的研究课题，在信息过滤、资料自动分类、生物信息学等领域得到广泛应用。
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