聚值集

词组短语

cluster set

与聚值集相关的网络例句 [注：此内容来源于网络，仅供参考]

Several cluster validity measures have been proposed for confirming the optimal k value. However, the existing methods may not work well for the following two kinds of data sets: the data set containing cluster groups with different densities and the data set in which the cluster groups are extremely close to each other.

目前，已经提出了许多确定最佳k值的聚类有效性方法，但这些方法都不能很好地处理两种数据集：类密度不同的数据集和类间距比较小的数据集。

This approach not only inherits the advantages of absolute density based algorithms which can discover arbitrary shape clusters and are insensitive to noises , but also efficiently solves the following common problems: clustering results are very sensitive to the user-deflned parameters, reasonable parameters are hard to be determined, and high density clusters are contained fully in coterminous low density clusters. With this approach, incremental clustering can also be supported effectively by defining the affected sets and seed sets of the updating objects in this approach.

聚类算法的有效性问题主要表现在三个方面：其一，聚类算法大多要求用户输入一定的参数，例如希望产生的簇的数目，而这些参数通常难以确定，特别是针对高维空间中稀疏分布的实际应用数据集，用户几乎无法给出合适的算法参数，因此非专业用户需要与数据分析专家密切配合才能保证获得理想的聚类结果，导致算法的使用极为不便；其二，聚类结果对于输入的参数值过于敏感J，往往参数值的一些轻微变化却产生聚类结果的很大差异；其三，对于高维的实际应用数据集，其数据分布往往是稀疏的、杂乱的，很难为算法选择全局的参数进行准确的聚类分析，使得聚类的质量难以保证。

Thus,for a given dataset,the result gives different clusters number when the lagrange multiplier is not the same.

这样，在对同一组数据集进行聚类时，调控因子值不同，结果得到的类数目就不同。

In this paper, a scale space clustering algorithm based on mathematical morphology operators is proposed. The data are firstly converted into a binary image, the noises are then deleted with close open operators. A scale space is constructed with the close operator and structure elements as well as increased size. The connected cells merge with each other with the increasing scale until all of them combine into one.

中文摘要：提出了一种基于数学形态学算子的多尺度聚类方法：首先将数据进行二值图像化处理，利用一次闭开运算去除噪声干扰后再利用逐步增大结构元素的闭运算构建尺度空间，图像内的连通单元集随着尺度上升不断融合，最终全部归并。

In this method, all stable sub-intervals in the training sequence were located for each pixel as possible background. Then a classify data set was constructed by the median values of each subinterval. A background subset was obtained from the data set by unsupervised clustering. Accordingly the initialized background was obtained from the background subset.

首先利用滑动可变窗口检测每个像素的所有不重叠平滑子序列，获取可能背景；然后选择每个平滑子序列的中值样本点构建分类序列集，根据未知类别的无监督聚类识别思想获取背景子集，实现背景初始化。

The practicality of using rough set to reduce the date was discussed, in this paper, and the interval-valued continuous attribute discretization by applying self-organizing map neural network clustering was proposed, too. This article proposes a simplified indiscernible matrix's method, which reduces the example's condition attribute and eliminates the redundant information of the date. What's more, it provides the methods used to diagnosis compressor's faults based on the intelligence hybrid system by adopting the MATLAB neural network workbox. At last, the functional modules which make up into intelligence hybrid system for fault diagnosis was introduced, including: data acquisition module, date preprocessor module, date reduction module, neural network module, fault diagnosis module and fault expressing module. Here, the article also gives the flow of work of all the above modules.

论文论述了应用粗集对故障数据进行约简的可行性，并应用自组织映射神经网络的聚类功能，来实现连续属性值的离散化方法；给出了简化的分明矩阵法，并用该方法实现了样本条件属性的约简，消除了样本数据中的冗余信息；采用MATLAB神经网络工具箱建立了压缩机的部分故障类型的智能混合诊断系统；最后介绍了智能混合故障诊断系统的功能模块组成，各功能模块分别为：数据采集模块，数据预处理模块，数据约简模块，神经网络模块，故障诊断模块，故障解释模块；并给出了各功能模块的工作流程。

Automatic color transfer effectively solves this issue. Carve up Roseau and subtractive clustering is applied in this paper. Completely automatic algorithm implementation is realized. In clustering, pixels are selected according to their brightness, and those with larger data density are cluster to sample block. First of colors in the same block are transferred first, others are transferred later.

本文将网格划分减法聚类应用到颜色迁移中，完全实现算法的自动化，聚类方法按照亮度值的大小依次聚类，从各个聚类域中提取出数据点密度较大的像素点集组成样本块，首先对每个样本块间进行颜色迁移，再完成样本块以外的其他像素的颜色迁移。

与聚值集相关的网络解释 [注：此内容来源于网络，仅供参考]

cluster set：聚值集

cluster sampling 分组抽样 | cluster set 聚值集 | coadjoint functor 余伴随函子

curvilinear cluster set：曲线聚值集(合)

curvilinear angle | 曲线角 | curvilinear cluster set | 曲线聚值集(合) | curvilinear cone | 曲线型(扬声器)纸盆

greatest lower cluster set：最大聚值集

greatest lower bound 最大下界,最大下限 | greatest lower cluster set 最大聚值集 | greatest nilpotent ideal 最大幂零理想

coadjoint functor：余伴随函子

cluster set 聚值集 | coadjoint functor 余伴随函子 | coalgebra 上代数

greatest lower bound：最大下界,最大下限

greatest limit 最大极限 | greatest lower bound 最大下界,最大下限 | greatest lower cluster set 最大聚值集

greatest nilpotent ideal：最大幂零理想

greatest lower cluster set 最大聚值集 | greatest nilpotent ideal 最大幂零理想 | greatest north latitude 最大北黄纬