模糊c均值算法(FCM)是经常使用的聚类算法之一。
The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
论文采用了一种基于改进的模糊C均值算法来聚类图像。
This paper proposes a modified fuzzy C-means (MFCM) clustering algorithm to cluster all images before retrieval.
本文讨论了模糊聚类中的模糊C均值算法和聚类有效性测度。
This paper discusses the fuzzy C-means algorithm (FCM), one of the fuzzy clustering methods and clustering validity measurements.
提出一种基于球形的模糊c -均值算法的中文文本聚类方法。
A clustering algorithm for Chinese documents based on the spherical fuzzy c-means algorithm is presented.
归一化加权平均值算法以其灵活性和可靠性将得到广泛的应用。
The normalized weighted average algorithm will surely find a wide application due to its flexibility and reliability.
使用模糊c均值算法时，如何选取模糊指标m一直是一个悬而未决的问题。
It is an open problem how to select an appropriate fuzziness index m when implementing the FCM.
实验结果表明，基于自适应权重的粗糙K均值算法是一种较优的聚类算法。
The experiments indicate that the rough K-means based on self-adaptive weights is an effective rough clustering algorithm.
传统的K均值算法的初始聚类中心从数据集中随机产生，聚类结果很不稳定。
The initial clustering center of the traditional K-means algorithm was generated randomly from the data set, and the clustering result was unstable.
该方法采用PCA提取动作电位特征，使用改进K均值算法实现动作电位分类。
The action potentials' features are extracted by PCA, the action potential classification is implemented by the improved K-means algorithm.
研究了基于聚类分析的非监督式异常检测方法，并改进了K均值算法用于聚类分析。
Researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm.
均值算法的聚类个数k需指定，聚类结果与数据输入顺序相关，而且易受孤立点影响。
K-means algorithm has some deficiencies. The number K must be pointed and its effectiveness liable to be effected by isolated data and the input sequence of data.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
本文利用改进的K -均值算法对缺失数据进行处理，提高了朴素贝叶斯分类的精确度。
This paper USES the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.
采用网格索引来组织地形数据，利用反距离高程加权平均值算法，提取TIN地形的特征点。
Terrain data is organized by grid, the article adopts height weighted average algorithm of elevation based on grid network, to extract feature point of TIN terrain.
传统K均值算法对初始聚类中心敏感，聚类结果随不同的初始输入而波动，容易陷入局部最优值。
Traditional K-Means algorithm is sensitive to the initial centers and easy to get stuck at locally optimal value.
基于K -均值算法的模糊分类器具有很好的分类效果，用它可以很准确的对训练样本进行分类。
For increasing classifiers classification rate, We make use of the fuzzy theories to K-means algorithm again.
传统的K-均值算法选择的相似性度量通常是欧几里德距离的倒数，这种距离通常涉及所有的特征。
The Euclidean distance is usually chosen as the similarity measure in the conventional K-means clustering algorithm, which usually relates to all attributes.
运用此算法和传统均值算法对激光雷达数据进行了处理，并且使用多种指标对处理结果进行了比较。
The algorithm and mean filtering algorithm are applied in lidar data, and their results are compared in different evaluation parameters.
针对模糊c -均值算法在汽轮机故障诊断中的不足，提出了粒子群优化加权模糊聚类分析的方法。
Aimed at the disadvantages of fuzzy C-means in fault diagnosis of steam turbine set, a weighted fuzzy clustering method based on particle swarm optimization is put forward.
实验证明新算法有效解决了调和K均值算法中簇个数需事先给定及聚类算法容易陷入局部最优的问题。
The result of experiment indicate that the new algorithm efficiently resolves the problems of KHM algorithm that the count of clusters need decide prior and it well reach local optimum result.
并考虑在一维分割特征向量情况下，通过引入直方图统计特性，实现了模糊C-均值算法的快速运算。
Under considering 1- D segmentation character vector, the histogram is introduced, and the speed of F CM is greatly increased.
通过对模糊c均值算法聚类特性的分析，引入了约束函数及模式相似度的概念，提出了改进的FCM算法。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM is presented.
利用数据点的密度大小作为权值，借助数据本身的分布特性，提出了一种点密度加权模糊c -均值算法。
A dot density weighted fuzzy C-means algorithm is proposed by using density size of data dot regarded as weighted value and distributing characteristic of datas own.
然而，由于聚类初始中心点选择的随机性，传统K -均值算法以及其变种的聚类结果会产生较大的波动。
However, owing to random selection of initial centers, unstable results were often obtained while using traditional K-means and its variants.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
结合模糊c均值算法(FCM)与模糊最小最大神经网络算法，提出一种基于超长方体集的模糊模式识别算法。
Based on fuzzy C-Means algorithm (FCM) and fuzzy Min-Max Neural Networks, an integrated algorithm for fuzzy pattern recognition using hypercube set was proposed.
同otsu算法和灰度平均值算法比较，该图像二值化方法在具有小区域缺陷特征图像处理方面有一定的优势。
Compared with the Mean Gray Level and OTSU algorithm, the binary conversion method has advantages of processing small-area defect images.
同otsu算法和灰度平均值算法比较，该图像二值化方法在具有小区域缺陷特征图像处理方面有一定的优势。
Compared with the Mean Gray Level and OTSU algorithm, the binary conversion method has advantages of processing small-area defect images.
应用推荐