知识聚类

详细分析知识组织的7种方法：知识表示、知识重组、知识聚类、知识存检、知识编辑、知识布局和知识监控；

In addition , it analyzes seven approaches to knowledge organization , namely , knowledge representation , knowledge reorganization , knowledge clustering , knowledge storage and retrieval , knowledge editing , and knowledge distribution and supervision .

本文探讨了企业知识门户的4项知识组织任务：知识获取、知识表示、知识聚类与分类、知识检索。

The article discusses the 4 knowledge organization tasks of the enterprise knowledge portal , that is , knowledge acquisition , knowledge representation , knowledge clustering and classification , and knowledge retrieval .

一种基于Windows调色板和知识聚类的彩色磨粒图像分割方法

An Approach of Color Wear Particle Image Segmentation on the Windows Palette and Knowledge based Classification

以芥子气为例，利用知识聚类法收集信息，并分析其特征；

Taking mustard gas as an example , the information was collected using knowledge agglomeration , and its characteristics were analyzed as well .

ILS中知识的聚类和表示

Knowledge representing and clustering in intelligent learning system

同时利用图论知识、聚类分析方法提出了基于系统核与核度理论的小城镇可持续发展指标筛选模型。

We also established a method of index selection on base of system core and coritivity theory , the existing index of small towns'sustainable development can be simplified by using the model .

FCM（fuzzyC-Means，FCM）算法是一种基于目标函数优化的模糊聚类方法，其收敛结果依赖于聚类原型参数的先验知识（即聚类中心和聚类数）。

Fuzzy C-Means algorithm is a based on objective function is the vague and class methods and the result is dependent on the clustering of a parameter previous experience knowledge ( clustering center and clustering number ) .

该模型结合Snort规则的特征和数据挖掘中的知识，提出聚类泛化和最近邻泛化两种新的规则泛化方法来改进规则，增强Snort的检测能力，从而达到识别更多入侵行为的目的。

In the new model , combining the characteristics of Snort rules and algorithms in data mining , both cluster generalization and nearest neighbor generalization were also proposed to enhance the detection ability of rules and achieve the goal of detecting more intrusions .

半监督聚类利用少量的先验知识，协助聚类分析，提高聚类的准确率或效率。

In semi-supervised clustering , a small amount of a priori knowledge are used to assist in clustering result seeking , improving clustering accuracy or effeciency .

为提高聚类算法的稳定性，相关学者提出了聚类集成技术，而传统的聚类集成方法不能利用先验知识来指导聚类集成过程，为更好地提高聚类集成的性能，半监督聚类集成技术应运而生。

To improve the stability of the clustering algorithm , some scholars have proposed clustering ensemble technology , but the traditional clustering ensemble method cannot use the background knowledge to guide the integration of clusterings .

再者是提出了项目知识本体的聚类方法与比较技术，支持实现相似项目的机器自动查询与项目创新判定的机器协助，支持项目自动分类评价，有效提高了评价的科学性。

Moreover it proposes clustering method and comparing method of project knowledge ontology , in support of machine automatic inquiry of similar projects and machine help of determination of project innovation , as well as classification evaluation of project effectively raising the scientificity of the evaluation .

本文提出基于代理的协同推荐技术，充分考虑了最大限度的利用系统收集的知识，利用C-Means聚类得到合成的代理代替传统算法中的邻居，为用户提供推荐，显著地提高了协同推荐的预测精度。

This thesis proposed collaborative filtering based on multi-agent . We make the full use of knowledge collected by the system and apply C-Means clustering technique to get agents to take the place of neighbours .

基于领域知识的半监督聚类算法研究

A Study on Semi-supervised Clustering Algorithm Based on Domain Knowledge

知识发现中的聚类分析及其应用

Cluster analysis in KDD and its applications

智能学习中的知识表示和知识聚类

Knowledge Representing and Clustering in Intelligent Learning

不完全知识下的概念聚类

Concept clustering under insufficient knowledge

本文主要是基于概念格和粗糙集的知识来解决文本聚类问题。

This article is mainly research the problem of text clustering based on the concept lattices and rough set .

提出一种将已知样本的先验知识融合到模糊聚类过程中的半监督循环迭代聚类模型，较为有效地克服模糊聚类为无监督模糊识别存在的弱点。

At last , a semi-supervised cross iterative fuzzy clustering algorithm , with the integration of transcendental knowledge into clustering , is proposed , which can effectively handle the weakness of unsupervised fuzzy recognition .

数据挖掘技术通过对数据库中的数据进行挖掘，可以得到很多重要的知识，包括分类知识、聚类模式、关联规则以及序列模式等等。

Data mining technology can get much important knowledge from mining the data in the database , including classification knowledge , clustering patterns , association rules , and sequential patterns and so on .

通过对噪声样本进行数据挖掘和模式识别，在线学习噪声的实时特性，获得其概率密度函数的先验知识，应用此先验知识进行样本聚类、分类、无监督学习并对噪声参数进行精确估计。

The pre-knowledge of the probability density function is used in the noise sample 's classification , clustering and unsupervised parameter estimation .

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。

Data Mining mainly studies on research Generalization Knowledge , Association Knowledge , Classification Knowledge , Clustering Knowledge , Prediction Knowledge , and Deviation Knowledge .

采用一种反映知识模块间关系的知识混合结构方式，通过对知识的规范录入和基于关联规则的知识聚类，得到一系列相关的知识项。

This paper presenteds a combined knowledge structure which reflecteds the relationships among knowledge modules . A series of association knowledge items was obtained by standardizing input ways and knowledge clustering based on association rules .

在深入分析现有知识管理系统的基础上，本文提出了两种以主题为中心进行知识组织的途径：有样本的知识组织自动分类以及无样本的知识聚类。

After in-depth study of the existing KM ( Knowledge Management ) systems , this paper brings forward two kinds of methods for the Organization of Knowledge based on the subject : sampling Knowledge Organization Automatically Classification and the non-sampling Knowledge Clustering .