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用matlab实现K均值聚类算法
- 用matlab实现K均值算法
数据挖掘中K均值算法实现
- 数据挖掘中K均值算法的实现用MATLAB编写-data mining to the K-means algorithm to achieve prepared using MATLAB
数据挖掘中FUZZY_K均值算法实现
- 数据挖掘中FUZZY_K算法实现（用MATLAB实现）-FUZZY_K data mining algorithm (MATLAB)
matlab_coding
- matlab的源程序,没有其他目的，只希望下载k均值算法看看!-Matlab source, no other purpose only hope downloaded k means algorithm to see!
K-Mean1
- 编写K-均值聚类算法程序，对下图所示数据进行聚类分析(选k=2)-prepare K-means clustering algorithm, the data shown in the chart below cluster analysis (EAC k = 2)
fk-means
- 数据挖掘中模糊k均值算法，matlab工具编写。-data mining fuzzy k-means algorithm, Matlab tool for the preparation.
korcmeans
- 对于模式识别中的k均值（也称c均值）问题，给出了matlab实现。-pattern recognition for the k-means (also called c-means), is the realization of Matlab.
matlab_kjunzhi
- 基于matlab的图像K均值聚类算法程序 -Matlab-based Image K-means clustering algorithm procedures
kmean
- matlab程序，用于人脸识别的K均值算法程序，非常有用。-matlab program for K-means algorithm for face recognition program, very useful.
k-mean
- k-均值算法 做的图象分割实例 （将图片像素聚成三类）-k-means algorithm to do the image segmentation example (poly-pixel pictures into three categories)
k_uiniform
- k均值算法的matlab程序，可直接运行。-k-means algorithm matlab procedures, can be directly run.
K_Average
- K均值算法支持向量机,很使用可靠,看了一定不会后悔的-K-means algorithm for support vector machines, it is the use of reliable, read will not regret it
K-means
- 简单实用的k均值聚类算法，可以实现多位向量的简单聚类-Simple and practical k-means clustering algorithm, can achieve more than a simple vector clustering
K
- K-均值聚类算法，对数据进行聚类分析，可用于提取关键帧等。用matlab实现-K-means clustering algorithm, cluster analysis of data that can be used, such as key fr a me extraction. Using matlab to achieve
K-means_Matlab
- K-均值算法的Matlab源代码，比较简短-Matlab source code of K-means algorithm
kmeans
- k均值图像分割，经典的图像分割方法，算法简单，效果好。-k-means image segmentation
k-means
- k均值聚类算法源码 聚类算法学习的实例功能-k-means cluster algorithm
k-meanswunn061
- matlab 实现k均值 k-means算法 过程清晰 适合初学者-matlab to achieve k-means k-means algorithm suitable for beginners a clear process
K-MEANS-MATLAB
- 用matlab7.0编写的k均值算法，参数可调节，很好用-K-MEANS MATLAB
K—均值聚类提取
- k均值聚类提取，适合学习。先将RGB图像转换到LAB空间，在LAB空间进行聚类分割。(K-means clustering is suitable for learning. First convert the RGB image to LAB space and perform clustering and segmentation in the LAB space.)