搜索资源列表
KMeans
- K均值算法，Matlab实现，输入分类数，实现分类-K means algorithm, Matlab implementation, enter the number of classifications to achieve classification
c_modify
- 在matlab环境下利用c均值聚类方法解决非监督分类问题-Matlab environment in the use of c-means clustering method to solve the problem of non-supervised classification
kmean
- 包括K-均值聚类算法的思想介绍，kmeans的MATLAB代码，c语言代码、c++代码。-Including the K-means clustering algorithm introduced the idea, kmeans of MATLAB code, c language code, c++ code.
kmeans-image-segmentation
- K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm
pso-clustering
- 基于粒子群的改进K均值聚类算法源代码。适用于MATLAB7.1。-Improved PSO-based K means clustering algorithm source code. For MATLAB7.1.
K-Means
- 较简单的KMeans聚类算法实现，编程语言matlab-Clustering KMeans relatively simple algorithm, programming language matlab
123456789K_Average
- K均值算法的matlab代码，使用起来很简单，对于模式识别的效果也很好，初学者也很容易看懂。-K-means algorithm matlab code, very easy to use, for the effect of pattern recognition is also good, beginners can easily understand.
unsupervisedClassification
- 非监督分类程序，MATLAB环境，采用K均值算法，通过初始聚类中心逐次迭代而得到所要分类，并输出分类后的图像。-Non-supervised classification procedures, MATLAB environment, using K-means algorithm, the initial cluster center through successive iterations to be classified, and the output classification im
C-means
- 使用c-均值聚类算法在IRIS数据上进行聚类分析，随机选择三个初始聚类中心，经过多次迭代，最终将150个样本分为三类。-Use c-means clustering algorithm in the IRIS data on the cluster analysis, three randomly chosen initial cluster centers, through a series of iterative, 150 samples will eventually fall into
K_average
- matlab实现的k均值聚类算法，可以实现对大量数据的有效分类-matlab implementation of the k-means clustering algorithm, can achieve a large amount of data on an effective classification
Cmeansalgorithmmatlabprocessprocedures
- C均值法的程序算法matlab 程序，本程序用MATLAB实现了聚类分析的功能，保存tex文件中，无数据-C-means algorithm matlab process procedures, the procedures used MATLAB implementation of the cluster analysis function, preservation of tex file, no data
K-means_Matlab
- K-均值算法的Matlab源代码，比较简短-Matlab source code of K-means algorithm
fcm
- 通过模糊c-均值（FCM）聚类实现图像的分割。-Through the fuzzy c-means (FCM) clustering to achieve image segmentation.
imgkmeans
- 将K均值算法用于图像分割,输入的是彩色图像,转换为灰度图像进行分割,输出结果为灰度图像.利用灰度做为特征对每个像素进行聚类,由于光照等原因,有时应该属于一个物体的像素,其灰度值也会有很大的差别,可能导致对该像素的聚类发生错误.在分割结果中,该物体表面会出现一些不同于其它像素的噪声点,因此,算法的最后,对结果进行一次中值滤波,以消除噪声,达到平滑图像的作用-The K means algorithm for image segmentation, the input is a color imag
matlab-code
- 模式识别c均值聚类，也陈k均值，是模式识别中最最要的聚类方法之一。-Pattern Recognition, c-means clustering, and Chen k means, is far the most to the clustering pattern recognition methods.
KMEANS
- K-Means动态聚类算法源程序。可以用来发现社团结构。-Dynamic K-Means clustering algorithm source code. The structure can be used to find associations.
FCM
- 在matlab平台下实现的FCM（模糊C均值）聚类分割-In the matlab platform to achieve FCM (fuzzy C mean) clustering segmentation
kmeans
- 一种改进的均值聚类算法，能很好的利用与图像分割技术-k-means cluster
C_FCM
- 用C均值聚类方法实现图像分割，matlab实现，包含实验报告-C means clustering method used to achieve image segmentation, matlab implementation, including test reports
04657872GAFCM
- 遗传算法改进的模糊C-均值聚类MATLAB源码.模糊C-均值算法容易收敛于局部极小点，为了克服该缺点，将遗传算法应用于模糊C-均值算法(FCM)的优化计算中，由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最优分类结果。(Improved genetic algorithm and fuzzy C- means clustering MATLAB source. The fuzzy C- means algorithm is easy to converge to local m