实验表明，网络的逼近程度取决于聚类数的多少。
The simulation result shows that the approximation capability of the network depends on the number of clusters.
自动确定聚类数和海量数据的处理是谱聚类的关键问题。
Ascertainable clustering number and large training sets are vital problems of spectral clustering.
前言：目的探讨颅脑mri图像模糊聚类分割算法中最佳模糊聚类数。
Objective: To discuss the best fuzzy clustering number of MRI brain images segmentation.
聚类有效性指标既可用来评价聚类结果的有效性，也可以用来确定最佳聚类数。
Cluster validity index is used to scale the validity of clustering, and determine the number of clusters.
结果当模糊聚类数为5 ~6时，模糊聚类有效性函数最小，图像处理的效果达到最佳水平。
Results: When fuzzy clustering number for 5-6 and fuzzy clustering validity achieved a minimum level of image processing with the best effect.
为解决聚类数未知条件下面状地理实体的聚类问题，文中提出了一种基于聚类有效性函数的聚类方法。
A cluster validity function-based method is proposed for solving the problem of clustering for area geographical entities when the number of cluster is unknown.
将该种模型运用于公开的白血病基因表达数据集进行实验，实验表明该方法能自动获取基因表达数据的聚类数，并得到较高的分类准确率。
We applied the model to analyze the expression data set of leukaemia. The experimental result proved that this model can get cluster Numbers automatically and a high accuracy of classification.
以图像的布朗维数为纹理特征对编码中的图像块进行聚类和排序，实现了对每个值域块所需比较定义域块数目的精确控制。
Taking the Brownian dimension as their texture feature, image blocks were clustered and sorted, to control the number of domain blocks to be compared with each range block in coding.
这种无监督的聚类方法能够自动搜索最佳的网络输出节点数而获取图像中的目标数，从而完成对图像的自动分割。
This kind of unsupervised clustering method can search for the optimal number of output nodes automatically to get the number of textures in the 'image, and finish the automatic segmentation.
该模型利用模糊聚类技术确定系统的模糊空间和模糊规则数，利用BP算法调整模糊神经网络的权系数。
The fuzzy space and the number of fuzzy rules of this model are defined by the fuzzy clustering method and weight coefficients of the model are adjusted by the BP algorithm.
对大词汇量汉语连续语音识别的实验结果表明：高斯模糊聚类使高斯数减少25%时，识别率提高了0.15%。
The experimental results on large vocabulary continuous Mandarin speech recognition show when the number of Gaussians is reduced by 25%, the recognition accuracy increases by 0.15%.
建立了确定轧制单元数的一种新的聚类模型，开发出一种有效的两阶段算法(启发式算法和模拟退火算法)求解此问题。
A new clustering model is established to determine the number of rolling units, then a two-phase algorithm (heuristic and simulated annealing) is developed to solve it.
将信息获取能力分指标取数域延拓，求得相关对象关于灰类的综合聚类系数。
The number field continuation was adopted by information acquire ability sub index, and the gray comprehensive clustering coefficients of relative objects were also acquired.
利用算术加权平均数法（UPGMA）对香果树居群进行聚类，结果9个居群可分成浙江省内和省外两大类群。
Using unweighted pair group method with arithmetic average (UPGMA), 9 populations of Emmenopterys henryi were clustered into two groups, inner-province group and outer-province group.
利用两个聚类效果评价指标模糊效果指数FPI和归一化分类墒NCE，确定了最适宜的分区数。
Performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimum cluster number.
探讨了聚类分析这一重要的数据挖掘方法在综合评价中的应用，将模糊聚类与综合评价相结合以解决待评价方案数较多的排序问题，并且文中还改进了建立模糊相似矩阵的方法。
Fuzzy clustering is associated with comprehensive assessment in the study of sorting when the number of object is large, and we improve the method of setting up fuzzy similar matrix.
针对一类特征指标值及指标权重均为三角模糊数的多指标信息聚类问题，提出了一种新的最大树聚类分析方法。
With respect to multiple attribute clustering analysis problems with triangular fuzzy numbers, a new clustering analysis method is proposed.
实验结果显示，该算法在不同结构和维数的数据集上都取得了更稳定的聚类精度。
Simulation results show that the algorithm can achieve more stable clustering accuracy on the benchmark data sets.
通过与层次聚类算法的比较，证明边介数聚类算法在肿瘤基因功能模块聚类方面具有一定的有效性和实用性。
In the end, through a real example the shortest route, its length and betweenness were worked out according to the algorithm.
通过计算两路肌电信号的分形维数，发现不同动作的肌电信号具有不同的聚类分布。
Two channel EMG Signals during four types' of forearm motions were analyzed to calculate their fractal dimensions. It was f…
第四章我们主要讨论了一类与空间中的聚点有关的分形集合豪斯道夫维数方面的性质。
In Chapter 4, we consider the Hausdorff dimension property of a class of fractals associated with some accumulation points.
当数据维数很高时，传统聚类算法也面临挑战：随着维数的增加，计算量迅速增大；
The traditional clustering algorithm is facing challenges, when the dimensional of data clustering is high:with the dimension increased, the calculated quantity rapidly does;
当数据维数很高时，传统聚类算法也面临挑战：随着维数的增加，计算量迅速增大；
The traditional clustering algorithm is facing challenges, when the dimensional of data clustering is high:with the dimension increased, the calculated quantity rapidly does;
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