Secondly, a new projection index based on dynamic cluster rule is constructed in the PPDC model, which would finish the sample clustering based on the projected characteristic value.
以投影寻踪理论为基础，利用动态聚类方法构建投影指标，建立了水资源评价的投影寻踪动态聚类模型。
Secondly, a new projection index based on dynamic cluster rule is constructed in the PPDC model, which would finish the sample clustering based on the projected characteristic value.
在一个简单投影指标下，用新的优化途径建立了多元数据分类模型，并将其用于多指标的标准水质分类。
We have evaluated this clustering for several sample queries that restrict their search to a single group_PDB value (i.e., "HETATOM") and found that it can improve query performance fourfold.
我们已经通过几个将搜索限制于单个group _ PD b值(即hetatom)的样例查询评估了这种集群并发现它能够将查询性能提高四倍。
Tomcat-cluster: Web-tier clustering, load balancer, and failover sample for the embedded Apache tomcat Web container.
tomcat - cluster：嵌入式Apachetomcatweb容器的We b层集群、负载平衡和故障转移示例。
We screened a random sample of 1063 subjects of all age groups selected by clustering.
我们筛选了随机抽样的由所有年龄组组成的1063个受检者为调查对象。
With the use of random access technology and some new algorithms, the clustering analysis of large sample can be processed on microcomputer. Thus practicality of this program system is improved.
由于采用随机存取技术和一些新算法，使本系统可在微机土对大样本问题进行聚类分析，从而提高了系统的实用性。
The author presented a method for morbid sample recognition that base mode clustering, paper proposed a eliminating algorithm of voting that base mode similarity calculating.
本文提出一种以模式聚类为基础的病态样本判定方法，并给出基于模式相似度计算的投票剔除算法。
A cluster dendrogram of the sample was constructed using average linkage clustering.
用平均连锁聚类法构建了样本的遗传相关聚类图。
By introducing the subtraction clustering algorithm, the sample data are classified and the local model parameters are identified off-line using the corresponding data set.
引入减法聚类算法对样本数据进行分类，用得到的分类数据对局部模型参数进行离线辨识。
Selecting train sample on the basis of fuzzy C-mean clustering can improve accuracy of train sample, singleness of train samples can be satisfied.
在模糊c -均值聚类的基础上选择训练样本，可以提高训练样本的准确度，满足了训练样本所需的单一性原则。
Then, the center of clustering could be gained by using the method of clustering and the typical sample was obtained.
然后利用聚类分析方法求得各类样本的聚类中心，得到典型样本。
We delt mainly with the fitting problem with known ancestors and pointed out that the clustering of ordered sample is a special case of this model.
着重介绍了具有已知先代的拟合，并指出有序样品的聚类为该模型的应用特例。
Then we introduce how to identify the TS model from sample data using fuzzy clustering algorithm and equivalent feedforward neural network.
然后介绍了如何使用模糊聚类算法和等价的前馈神经网络从样本数据中辨识离散的TS模型。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
Selecting train sample on the basis of fuzzy C-mean clustering decreased subjective factor affecting selecting train sample, so higher classification accuracy can be achieved.
同时，在模糊C-均值聚类基础上选择训练样本比起直接基于真实地物图选择，减少了主观因素对训练样本选择的影响，因此取得了更高的分类精度。
Meanwhile optimization theory based HCM clustering algorithm is used to cluster sample data to determine the number of node of hidden layer, so that the efficiency of RBF network in use is high.
同时采用基于优化原理的HCM算法实现聚类过程，来确定R BF网络的隐含层节点数，使网络的利用效率较高。
Objective Six familiar conditional hierarchical clustering methods were discussed, and some methods of 2-dimensional ordinal sample were selected which results were relative even.
目的探讨六种常见的条件系统聚类法的性质，并选择一到两个适于二维有序样品聚类的样品个数比较均匀的条件系统聚类法。
Applying the gray fixed-weight clustering method to evaluate the safety level of the sample road networks which was given in the chapter 3.
利用3种路网在各种类型的攻击情况下的失效程度的数据，应用灰色定权聚类判别法对其进行了计算，以得到各种类型路网的安全等级。
According to the observations of a sample of indicators, a measure of samples to choose between the targets of the similarity measurement method and use it as the basis for clustering;
根据抽样指标的观测值，选择某种度量样品之间的指标相似度的度量方法，并以此为聚类依据；
Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, the method can be used to classify and design the sample workpiece automatically.
该方法借鉴了神经网络理论、模糊聚类算法和自适应模式识别法的优点，自动完成样本的分类与样件设计工作。
Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, the method can be used to classify and design the sample workpiece automatically.
该方法借鉴了神经网络理论、模糊聚类算法和自适应模式识别法的优点，自动完成样本的分类与样件设计工作。
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