cluster number造句

• When they come to how to confirm the cluster number , they either give no answer , or enumerate it
  至于聚类数的确定问题，要么未给出答案，要么使用穷举法。
• So the current question is whether we can confirm the cluster number directly , not using any assumptions
  因此现在的问题是我们能否比较方便地直接确定聚类数，而不需任何假设。
• When the a system routes a message it processes the point code ? ottom up ? member number , cluster number , network id
  系统路由消息时会按照成员号、群集号和网络id的顺序自底向上地处理点编码。
• The prominent advantage of neoren algorithm makes neoren fit for the applications sensitive to cluster errors rather than cluster numbers
  Neoren聚类算法的这一特点使得其非常适合于对聚类错误敏感、而对聚类数要求不高的应用领域。
• But , in this aspect , many content work has been done , so the paper will choose a right clustering rule based on the work of them , in order to confirm cluster number directly under no assumptions
  但是，在此方面，人们已经做了很多有意义的工作，所以本文将在前人的基础上选择一个恰当的聚类准则函数，以便在无任何假设条件的前提下比较简单地直接确定聚类数。
• As for fuzzy c - means clustering algorithm , we introduced the concept of validity function to solve problems about partial optimization and how to decide the cluster number . third , we borrowed the software of matlab and spss and did experiment on a set of data . the experiment showed that the matlab program was simple and the speed was quick so that it could be applied in large number of data
  在模糊c均值聚类算法中引入了有效性函数的概念，从而部分克服了模糊c均值聚类算法局部最优和无法确定聚类的类数的问题；其次，借助matlab和spss软件，以一组数据为案例，利用matlab编程，给出实验示例。
• To solve the problems of adaptive determinition of the cluster number , flexible objective function definition and approximate optimal computation in clustering analysis , a ga - based divisive hierarchical clustering algorithm ( gadhc ) was proposed , which integrated some features of divisive hierarchical clustering algorithm and binary genetic clustering algorithm
  摘要针对聚类中自适应确定聚类个数、目标函数灵活定义及优化的近似计算等问题，综合了分裂式层次化聚类算法能根据相似度阈值自适应地确定聚类个数的特点及二进制遗传聚类算法具有较强的搜索近似最优解能力及目标函数定义灵活的特点，提出了一种基于遗传算法的分裂式层次化聚类方法。
• The number of fuzzy cluster neuron is equal to the fuzzy cluster number , and the neuron output is one element of the membership matrix . 4 . use hybrid fuzzy neural network modeling method and recipe fuzzy cluster method , a robust method to recipe - changing was presented . a multi - continuous parameters and one - recipe to one - output fuzzy neural network was designed to model the fouling in batch process
  浙江大学博士学位论文4 、针对间歇过程中使用模糊神经网络建模和测量对于配方变化较为敏感的问题，使用配方模糊聚类方法和混合模糊神经网络建模方法，设计了一个由多个连续型操作参数输入和1个离散型配方变量输入的配方混合模糊神经网络，用于污垢的建模和预测。
• A novel dynamic evolutionary clustering algorithm ( deca ) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand . deca searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes ; at the same time , the convergence of clustering center parameters is expedited with the help of fuzzy c - means ( fcm ) algorithm . moreover , by introducing memory function and vaccine inoculation mechanism of immune system , at the same time , deca can converge to the optimal solution rapidly and stably . the proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient deca to identify fuzzy models . the effectiveness of the proposed fuzzy modeling method based on deca is demonstrated by simulation examples , and the accurate non - linear fuzzy models can be obtained when the method is applied to the thermal processes
  针对模糊聚类算法不适应复杂环境的问题,提出了一种新的动态进化聚类算法,克服了传统模糊聚类建模算法须事先确定规则数的缺陷.通过改进的遗传策略来优化染色体长度,实现对聚类个数进行全局寻优;利用fcm算法加快聚类中心参数的收敛;并引入免疫系统的记忆功能和疫苗接种机理,使算法能快速稳定地收敛到最优解.利用这种高效的动态聚类算法辨识模糊模型,可同时得到合适的模糊规则数和准确的前提参数,将其应用于控制过程可获得高精度的非线性模糊模型
• Various clustering methods have been brought forward according to the need of various areas . among them , the object clustering method is the most popular one . but they classify the data into clusters according to their attributes , then optimize the cluster centers and subjections which are under the assumption of the given cluster number
  人们根据不同领域的需要，提出了各种不同的聚类方法，其中最受欢迎的是目标聚类法，但是他们大多是假设在给定聚类数的前提下，根据待聚类样本的属性，优化类中心或隶属度，将它们划分到各个类中。
• Under this background , we firstly expatiated the classification , primary aspects , and applications in e - commerce of data mining . after we discussed popular clustering techniques in detail , an improved clustering algorithm , adaptive k - means , is presented on the basis of analyzing the advantages and disadvantages of the traditional k - means . the new algorithm is superior to the traditional one that it can determine the cluster number and the initial centroids adaptively , and reduce the blindness and subjectivity to some extent
  基于这种应用背景和前提，本文首先系统阐述了数据挖掘的分类、技术以及在电子商务领域的主要应用，重点讨论了其中的聚类技术；在分析现有主要聚类算法的优缺点的基础上，提出了传统k - means算法的一种改进算法? ? adaptivek - means聚类算法，该算法能够自适应地确定聚类的个数以及初始聚类中心，避免了在聚类数目选取上存在的主观性以及初始划分的盲目性，在一定程度上弥补了原算法的不足，并通过实验验证了该算法的有效性。
• In connection with the difference and distribution characteristic of the samples in sample space rs based on dga , a new self - adapted weight fuzzy omean clustering model of fault diagnosis of the power transformer based on the potential function is proposed . meanwhile , from the aspect of geometry characteristic of fc - divided in s dimension sample space , a method is proposed for the purpose of getting an effective adjacent radius , adaptive cluster number c and original cluster center of x sample set . for the diagnosis sample x , the property measure and diagnosis rule are proposed , which under the condition of potential density function that determine c number of optimal fuzzy cluster p1
  根据以变压器dga数据为特征量的样本空间各样本差异特性以及样本在空间r ~ s的分布特性，首次提出了基于势函数自适应加权的变压器绝缘故障诊断的模糊c -均值聚类模型；同时，从s维样本空间的f ~ c -划分几何特性出发，提出了一种求取样本集的类势有效邻域半径和自适应求取聚类数和聚类中心初值的方法；对一个待诊断样本，设计了基于类势密度函数意义下的属性测度和诊断准则。