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texture3
- 本程序在对图像进行纹理分析（基于共发矩阵的方法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (based on total fat matrix method), on the basis of access to different regions of the image texture features, these featur
textureA2
- 本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on
UPublic
- 一个用delphi 写的图像,K均值(K-means)聚类算法-Written with the image of a delphi, K mean (K-means) clustering algorithm
k_means
- k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into
PatternRecognition
- 对图像利用ＩＳＯＤＡＴＡ与Ｋ均值算法进行聚类分析-Clustering analysis for image by ISODATA and K-Mean Algorithm
k_means
- In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. It is similar to the expectation-max
DocumentCluster
- clustering k-mean java
procustesAlign
- Performs Procustes point alignment on a group of point sets. Method rigidly aligns, shifts, and scales points to reduce mean square error. Method is described in: B. Klare, P Mallapragada, A.K. Jain, and K. Davis, "Clustering Face Carvings: E
Image_Clust
- Title: Image Clutering K-means and Mean Shift) delphi implemetation Descr iption: This delphi program contains image clustering algorithms K-means and Mean shift. Contains 2 pas file for kmeans and mean shift. by chamika deshan
NewK-means-clustering-algorithm
- 珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，
Kmeans-julei
- 动态聚类的k均值算法,采用matlab编程,内有多个子程序，和一个主程序-dynamic clustering algorithm k mean using matlab
kmeans-program
- k-mean algorithm for clustering
K-MEANS
- 均值计算方法源码实现：分群的方法，就改成是一个最佳化的問題，換句话說，我們要如何选取 c 个群聚以及相关的群中心，使得 E 的值为最小。 -Method of calculating the mean source implementation: clustering method, based on the best change is a problem, in other words, how do we choose c a center cluster and related g
Datamining
- an intuitive implementation of the k-mean algorithme for data-sets clustering, you have to preprocess your data set as shown in the data.dat and data.dfn befor execute
yael_kmeans
- 数字图像中快速k均值聚类图像的实现算法，可以运行啊-Fast mex K-means clustering algorithm with possibility of K-mean++ initialization
kmean
- k mean for clustering in the matlab.cluster do in environment 3-dimontional.
Cpp1
- 距离与相异度，然后介绍一种常见的聚类算法——k均值和k中心点聚类-Distance and dissimilarity, and then introduce a clustering algorithm- k mean and k-medoids clustering
KMean
- KMEAN C# In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data sp
Km
- In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data space into Vo
kmeans
- kmeans methode (k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean)