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1)  Clustering number
聚类数
1.
To address the issue in fuzzy C-means algorithm (FCM) that clustering number has to be pre-defined,a clustering algorithm,F-CMSVM (fuzzy C-means and support vector machine algorithm),is proposed for automatic clustering number determination.
针对模糊C均值算法(fuzzy C-means algorithm,简称FCM)在入侵检测中需要预先指定聚类数的问题,提出了一种自动决定聚类数算法(fuzzy C-means and support vector machine algorithm,简称F-CMSVM)。
2.
Ascertainable clustering number and large training sets are vital problems of spectral clustering.
自动确定聚类数和海量数据的处理是谱聚类的关键问题。
3.
It presents a simple, easily implemented method on spectral clustering to automatically find the suitable value of clustering number and gives superior performance on experim.
但是,大多数已有的谱聚类算法需要事先给定聚类数
2)  number of clusters
聚类数
1.
For many clustering algorithms, it is very important to determine an appropriate number of clusters, which is called cluster validity problem.
对于许多聚类算法,决定合适的聚类数目至关重要,这称为聚类有效性问题。
3)  clustering coefficient
聚类系数
1.
The paper analyzed urban public transportation system by introducing some basic and important topological parameters such as degree distribution,average path length,clustering coefficient and betweeness and so on which were used in complex network analysis.
通过引入复杂网络分析中一些常见的重要拓扑参数例如度分布、平均路径长度、聚类系数和介数等来对城市公交系统进行分析,以西安市公交系统为例,计算并分析了这些拓扑参数,并从交通工程的角度对这些拓扑参数的具体意义进行了研究,最后从城市公交规划和环保的角度为城市公交系统的建设提出了一些建议。
2.
Some typical characteristics of the generated networks are carefully investigated through computer simulations,including the network diameter,the average path length,the degree distribution,clustering coefficient,degree correlation etc.
通过细致的计算机仿真模拟,初步研究了该网络的一些典型数字特征度分布、网络直径、平均路径长度、平均聚类系数、度关联系数等。
3.
We characterize this complex network topology by measuring the clustering coefficient.
该方法通过复杂网络研究生物体的拓扑结构,主要通过测量聚类系数(也可称:集团系数)构建网络的拓扑结构。
4)  cluster coefficient
聚类系数
1.
In contrast to the old model,it has a much bigger cluster coefficient which is in conformity with the real worl.
由此提出了一个改进的局域世界演化模型,生成的网络同时具有无尺度和小世界特性,其度分布也同样表现为幂率和指数之间的某种过渡,而相对于原模型,却具有大得多的聚类系数,这符合真实世界的情况,使得本模型更有应用价值。
2.
Using analytical computing,we get the expression of degree distribution,cluster coefficient and average path length,and find this network model possessing complex networks characteristics like scale-free and small-.
通过解析的方法,导出这一模型的度分布、聚类系数和平均路径长度,发现其具备复杂网络的无标度、小世界等特性,并通过数值仿真进行了验证。
3.
Through analyzing in depth the present grey cluster model,we think they can be used to exact cluster with respect to cluster objectives when there are not distinguished differences between grey cluster coefficients,which often come forth in practical economic phenomena.
在灰色聚类评估分析中,当灰色聚类系数无显著性差异时,按照已有的灰色聚类方法无法对聚类对象进行准确的聚类,而在实际研究中经常会遇到聚类系数无显著性差异这类问题。
5)  Data Clustering
数据聚类
1.
Using data clustering algorithm can effectively distinguish/identify the mode of gene expression data and categorize them.
利用数据聚类方法,有效地辨别/识别基因表示数据的模式,对它们进行分类。
2.
This paper is devoted to a novel data clustering approach based on generalized cellular automata (GCA).
现有的数据聚类方法仍存在着各种不足,聚类速度和结果的质量不能满足大型、高维数据库上的聚类需求。
3.
Data clustering is an important problem in data mining.
数据聚类是数据挖掘中的一个重要课题。
6)  Parameter cluster
参数聚类
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