Methods: We collected the data of 380 gastritis (Weiwantong) patients include symptoms, tongues and pulses, then used variables cluster analysis to analyse it.
方法：收集380例胃炎(胃脘痛)病例，记录症状、舌象和脉象等临床资料，对临床资料进行变量聚类分析。
Importance of variables for each cluster.
每个集群的偏差的重要性。
According to 152 indexes of China economic system for 11 years data we make self-organization models for all variables and core variables by cluster analyze.
本文采用中国经济系统的152个主要指标的11年数据，先后建立了全变量自组织模型和采用簇分析后的核心变量自组织模型。
Model analysis approach builds a social interaction model based on other economic studies, adding the social capital variables, and defining and constructing a space of cluster for derivative analyze.
模型浅析浅析策略是在其他经济学研究的基础上，构建一个社会互动模型，并加入社会资本变量，定义构造一个集群空间进行推导浅析浅析。
This paper is concerned with a method for dealing with the cluster for samples containing qualitative variables.
本文介绍一种方法，用于处理含有定性变量的样品的分类问题。
Methods: The variables were subjected to SPSS11.0 for Windows by multi-factor analysis of variance and cluster analysis.
方法：运用sPSS11。0统计分析软件对所取变量进行多因素方差分析和聚类分析。
A Hybrid fuzzy neural network modeling method was presented. On the basis of CCT fuzzy neural network and fuzzy cluster method, this method can deal with discrete variables input.
针对模糊神经网络不能接受离散标称变量输入的缺陷，在CCT模糊神经网络和模糊聚类方法的基础上，提出了一种混合模糊神经网络建模方法。
Methods Neuropsychological tests were carried out in 34 subjects by using clinical memory scales including 17 variables and Q type cluster analysis was performed.
方法对34例调查者进行临床记忆量表等17种神经心理测验，将结果进行Q型聚类分析。
Selected 18 characters of parasitic wasp and host as variables, through SPSS to cluster analysis of parasitic wasp. The result is match with the classical phylogeny affiliation of parasitic wasp.
选取寄生蜂的18个特征作为指标，利用SPSS对寄生蜂进行聚类分析，结果与传统的寄生蜂系统发育关系基本相符。
A method of cluster analysis for variables was improved based on the inner relationship of a random vector.
用两类所含指标全体所构成的向量的内在相关性度量来刻画两类间的距离，改进了一种指标聚类的方法。
A method of cluster analysis for variables was improved based on the inner relationship of a random vector.
用两类所含指标全体所构成的向量的内在相关性度量来刻画两类间的距离，改进了一种指标聚类的方法。
应用推荐