1) local density based clustering
局部密度聚类
2) local cluster
局部聚类
1.
Identification of local clusters:a model-based Moran s I test;
局部聚类的鉴别：一种基于模型的Moran s I检验
2.
Traditional Moran s I can identify the cluster trend,but it cannot identify the local cluster trend.
传统意义上的Moran's I只能检验出是否存在聚类,但不能检验出是否存在局部聚类。
3) local density
局部密度
1.
From the existing theory of the random medium model, every single local maximum point of a continuous random medium as the center point of each cave distribution area was taken, the two model characteristic quantities namely the local radius R and the local density p were introduced, simulated all sorts of different random cave medium models by the.
采用局部半径R描述溶洞介质在大尺度上的离散程度,采用局部密度p描述溶洞介质在各溶洞分布区中的局部空间密度。
2.
The boundary layer effect of the powder injection molding filling flow process was studied through local density measurement and SEM.
通过对注射坯进行局部密度测试及采用SEM方法观察注射坯截面上粉末-粘结剂的分布状态,揭示了PIM充模过程的边界层效应。
4) cluster density
聚类密度
1.
One of the drawbacks of the SOFM is that the user must select the map size in advance,especially the time-consuming search for the best matching unit in large maps,A new Growing Tree-Structured Self-Organizing Maps(GTS-SOFM) is proposed and the specific algorithm to implement clustering is given,by using cluster density to Measure Cluster Quality.
针对传统Kohonen自组织特征映射(SOFM)神经网络模型结构需要预选指定的限制,特别在大的映射网络中寻找最佳匹配结点是很耗时的问题,我们采用一种新的动态增长树型自组织特征神经网络(GTS-SOFM),给出了实现聚类的具体算法,并且使用聚类密度来衡量聚类效果。
5) density clustering
密度聚类
1.
DCMIS:Density Clustering-based Application to Medical Image Segmentation;
基于密度聚类的医学图像分割DCMIS
2.
To solve the problem that support vector machine(SVM) can only classify the small samples set,a new algorithm which applied SVM to density clustering is proposed.
为了解决支持向量机的分类仅应用于较小样本集的问题,提出了一种密度聚类与支持向量机相结合的分类算法。
3.
Algorithms based on density clustering,which has the better effect to the spatial database,is one of the most important techniques in clustering analysis.
提出了一种带有矢量性的密度聚类算法,具有约束聚类方向,减少候选点的特点。
6) density-based clustering
密度聚类
1.
AADDM filters the noise /attack data in source dataset and generates a pure training dataset by a top-down density-based clustering method; builds a lightweight and efficient i
提出了一种基于自顶向下密度聚类的训练数据集生成算法。
补充资料：线型低密度聚乙烯/低密度聚乙烯共混物
分子式：
CAS号：
性质：系由线性低密度聚乙烯与低密度聚乙烯组成的共混物，20％的低密度聚乙烯可显著改善线性低密度的加工性能，提高薄膜韧性和透光性，当低密度聚乙烯达50％时，可提高薄膜撕裂强度和断裂伸长率。可通过粒料直接掺混制备。主要用于吹塑农膜、包装膜及挤出包覆电缆。
CAS号：
性质：系由线性低密度聚乙烯与低密度聚乙烯组成的共混物，20％的低密度聚乙烯可显著改善线性低密度的加工性能，提高薄膜韧性和透光性，当低密度聚乙烯达50％时，可提高薄膜撕裂强度和断裂伸长率。可通过粒料直接掺混制备。主要用于吹塑农膜、包装膜及挤出包覆电缆。
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