Clustering analysis is one of the basic methods of the data mining and knowledge finding and it is a non - surveillance data classification method.
聚类分析是在无先验知识无指导下进行数据无监督分类的一种数据挖掘技术。
An approach of using human region color clustering and human motion knowledge to understand human motion in machine vision is introduced.
提出了利用人体区域彩色聚类和人体运动的相关知识，理解机器视觉中人体运动的方法。
The technique of clustering, classification and abstracting based on AI, and so - called "Knowledge Indexing" technique, seemed as good approaches to solve such problems.
基于人工智能的信息内容的自动聚类、分类和文摘，以及深层次的“知识检索”为迎接这个挑战提供了新的支撑技术。
The method revises the knowledge fuzzy and the abuse of informationaccessing and processing by combining the theory of rough sets with fuzzy clustering approach.
该方法很好的结合了模糊聚类法和粗糙集理论，对知识的模糊性以及相关信息获取及处理的弊端都进行了修正。
Fuzzy cluster is one of the branches of knowledge discovery in database (KDD). And neural network is a good tool for clustering.
模糊聚类是目前知识发现(KDD)领域中的研究分支之一，而神经网络是用于聚类的良好工具。
In addition, an approach of knowledge acquisition automatically was proposed based on clustering analysis and statistical theory.
同时，提出了基于模糊聚类分析和数理统计原理实现知识自动获取的方法。
Utilizing the grey clustering method, mathematics and mechanics knowledge, the safety states of DAMS under typical load combinations are analyzed and evaluated.
利用灰色系统中的灰色聚类方法，并结合数学、力学知识，对大坝在各种荷载组合作用下的安全性态作了分评价。
Clustering analysis is an important research field of data mining, through which we can find hidden knowledge behind mass data.
聚类分析是数据挖掘中的一项重要技术，通过聚类可以发现隐藏在海量数据背后的知识。
In this paper, a method of information clustering and concept association is shown, it is based on neural network, and it aims at inkling information searching in knowledge discovery.
针对知识发现中的信息模糊查询问题，提出了一种基于神经网络的信息聚类及联想实现方法。
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。
Clustering analysis is important part of data mining. It is an unsupervised learning process and it doesn't need prior knowledge about data set.
聚类分析是数据挖掘重要的组成部分，它是一种无监督的学习方法，不需要关于数据集的先验知识。
The results show that, in the case of lacking priori knowledge, fuzzy clustering analysis is superior to dynamic state clustering analysis when the number of samples is small.
结果表明：模糊聚类分析法对于先验知识较少、样本量不大时，性能较佳。
In the first part, we introduce the related background of data mining and its theories knowledge. Then we briefly summarize the related work of clustering analysis.
本文首先介绍了数据挖掘研究的相关背景及理论知识，对数据挖掘中的聚类分析的相关工作做了一个简要的概述。
The pairwise constraints are the most common prior knowledge, and many semi-supervised clustering algorithms are based on the type of constraints.
成对约束是先验知识中最普遍的，目前许多半监督聚类算法都基于此类约束形式。
For the lack of valuable prior knowledge in the image retrieval process, unsupervised clustering algorithms should be applied.
因为在对图像进行聚类分析时，通常缺少可资利用的先验知识，所以需要采用无监督的聚类算法。
CRM can use data mining technology to find useful and unknown knowledge, and classify customers by using a clustering tool.
CRM利用数据挖掘技术发现客户数据背后隐藏的、有用的、未曾预料的知识。
An ART2-based dynamic risk management model is proposed by using of clustering technique and neural network knowledge.
结合聚类技术和ART2神经网络技术提出一个基于ART2神经网络的动态风险管理模型。
This thesis proposes a new effective scene clustering method which can automatically decide the stop point without prior knowledge.
本文提出了一种新的有效的场景聚类算法，无需先验知识，自动决定算法停止点。
Compared to unsupervised clustering, semi-supervised clustering utilizes a small amount of given prior knowledge to guide the clustering process.
相比于无监督聚类分析，半监督聚类利用提供的少量监督信息协助指导聚类过程。
Due to the knowledge redundancy, the normally adopted traditional clustering method for customer classification suffers from low clustering quality.
由于知识冗余的存在，采用传统的聚类方法进行客户细分存在细分质量低的问题。
In previous studies, very few clustering ensemble algorithms considered the prior knowledge of the datasets.
在以前的研究中，很少有聚类融合算法考虑到加入这点。
In order to improve IDS's ability of generalization for knowledge of intrusion, a method is put forward that applies fuzzy clustering to obtain hierarchy generation for intrusion feature set.
为了提高入侵检测系统对入侵特征知识的归纳和概括能力，提出了将一种基于模糊等价关系的动态聚类方法应用于对入侵特征集进行层次聚类。
Knowledge-intensive industries such as technology and finance thrive on the clustering of workers who share ideas and expertise.
在人才聚集的地方，人们交流想法、分享专业知识，只有这样的地方，知识密集型产业例如科技和金融业才能繁荣兴旺。
Data Mining mainly studies on research Generalization Knowledge, Association Knowledge, Classification Knowledge, Clustering Knowledge, Prediction Knowledge, and Deviation Knowledge.
数据挖掘主要研究内容包括广义知识、关联知识、分类知识、聚类知识、预测型知识和偏差型知识的内容。
Main works are as follows. A clustering ensemble algorithm based on prior knowledge and spectral analysis is proposed.
具体工作如下：提出了一种基于先验信息和谱分析的聚类融合算法。
Then the membership matrix obtained by clustering algorithm was used to reduce attribute set. Finally, based on entropy, a knowledge acquisition method of fuzzy Rough Set (RS) was put forward.
进而将聚类得到的属性隶属矩阵用于属性约简，并提出一种基于信息熵的模糊粗糙集知识获取的方法。
Then the membership matrix obtained by clustering algorithm was used to reduce attribute set. Finally, based on entropy, a knowledge acquisition method of fuzzy Rough Set (RS) was put forward.
进而将聚类得到的属性隶属矩阵用于属性约简，并提出一种基于信息熵的模糊粗糙集知识获取的方法。
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