3) c-means clustering
c-均值聚类
1.
By using c-means clustering,an algorithm is presented to discretize condition attributes in a decision table.
采用c-均值聚类法将决策表中的连续条件属性进行离散化,用粗糙集处理离散化后的决策表系统得到简化规则,然后将规则集输入BP神经网络进行训练,并对测试集进行预测。
2.
It is very convenient to find out those advertising videos from others according to the shot change times per second by C-Means clustering method.
获得每个视频的直方图差分曲线后,调整去掉编辑特效造成的虚假峰,得到每个视频的每秒平均镜头切换次数,以此作为广告视频和非广告视频的分类依据,进行C-均值聚类。
3.
This paper presents a static background image reconstruction algorithm based on C-means clustering for video surveillance.
论文提出了一种基于C-均值聚类的视觉监控背景图像构建算法,实时构建灰度或彩色背景图像,实验验证了该算法的有效性。
4) C-means cluster
C-均值聚类
1.
The Research of a C-means Cluster by Genetic Algorithm;
基于遗传算法的一种c-均值聚类算法研究
2.
To increase the speed of image retrieval of the management system,proposes a content-based image retrieval algorithm which aims at improving the C-means clustering algorithm by selecting initial value of the cluster center and updating the classified center.
为了提高管理系统的图像检索速度,提出了一种基于内容的图像检索算法,从聚类中心初值选取和分类中心的更新方面改进C-均值聚类算法,较好地解决了图像的分类问题。
5) fuzzy c-means clustering
模糊C均值聚类
1.
The analog circuits fault diagnosis based on fuzzy C-means clustering;
基于模糊C均值聚类的模拟电路故障诊断
2.
Segmentation of fat and lean meat in beef images based on fuzzy C-means clustering;
基于模糊C均值聚类的牛肉图像中脂肪和肌肉区域分割技术
3.
Brain MR image segmentation based on anisotropic Gibbs random field and fuzzy C-means clustering model;
基于Gibbs场与模糊C均值聚类的脑MR图像分割
6) fuzzy C-means clustering
模糊C-均值聚类
1.
Escaped toll analysis of ETC system customer data based on fuzzy C-means clustering;
基于模糊C-均值聚类的ETC系统客户的逃费分析研究
2.
In order to recognize the pollutant sources and build the correspondence relationships between contaminated sources and important pollutants,a set of intelligent recognizing method based on correspondence factor analysis and fuzzy C-means clustering(short for IRM-CFA&FCM) is developed.
为识别东湖污染物来源,建立排污口与主要致污因子之间的对应关系,提出了污染物来源智能识别方法;该方法巧妙耦合了对应分析、模糊C-均值聚类及聚类有效性函数等方法,并用加速遗传算法有效解决了这一复杂问题。
3.
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。
补充资料：1,3-丁二烯低聚的均聚物
CAS：68441-52-1
中文名称：1,3-丁二烯低聚的均聚物
英文名称：1,3-Butadiene, homopolymer, oligomeric
中文名称：1,3-丁二烯低聚的均聚物
英文名称：1,3-Butadiene, homopolymer, oligomeric
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条