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kmeans
- k均值聚类方法。 在给定一个有n个对象的数据集，划分聚类技术将构造数据进行k个划分，每一个划分代表一个簇，k小于等于n。-k-means clustering method. Given a set of n objects data, dividing the data clustering techniques to construct k partitions, each partition represents a cluster, k less than or equal n.
kmeans1
- 实现对图片进行k均值聚类，也可以修改初始值，修改图片名直接可以运行-Implementation to k-means clustering of images, can also modify the initial value, the images can be run directly
k-means
- K均值算法，将数据矩阵命名为data，设置聚类簇个数k，可对多维数据进行聚类。-K mean algorithm, the data matrix is named data, set the number of clusters K, can be used to cluster the multi-dimensional data.
Desktop
- K均值聚类算法，对风电机组功率数据进行聚类分析，包括详细的程序说明。 只要把这两个文件放入一个空文件夹下，在MATLAB中执行m文件，就可得到聚类结果。-K-means clustering algorithm, the wind turbine power data clustering analysis, including a detailed descr iption of the procedures. As long as these two files into an empt
Kmeans
- 按照模式识别一书，实现k均值聚类的matlab版本代码-According to the book Pattern Recognition , implement k-means clustering matlab version of the code
Mymeans
- k-means（k均值聚类）,使用R语言实现，分类的准确度跟自带的差不多-k-means written by myself
k-means-by-LR
- 标准的数据挖掘聚类算法 k均值聚类 k-means聚类 严格按照标准算法执行 简单高效-Standard data mining clustering algorithm k-means clustering In strict accordance with the standard algorithm is simple and efficient execution
K-MEAN
- k-均值聚类算法的源代码，推荐的大家使用junzhinjuleisuanfa-k-junzhiyuandaima
K-Means-master
- 模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现-Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set
k_means
- 这是数据挖掘中的k均值聚类算法，用java语言编写的，对于搞聚类的人士很有帮助-This is the data mining k-means clustering algorithm, using java language, for persons engaged in clustering helpful
K_Means
- K-Means是聚类算法中的一种，其中K表示类别数，Means表示均值。顾名思义K-Means是一种通过均值对数据点进行聚类的算法。K-Means算法通过预先设定的K值及每个类别的初始质心对相似的数据点进行划分。并通过划分后的均值迭代优化获得最优的聚类结果。(K-Means is one of the clustering algorithms, in which K represents the number of classes, and Means means the mean. As t
k-means-for-iris
- 利用K均值聚类对鸢尾花样本进行聚类的matlab程序，包含源代码、样本数据、聚类结果(The matlab program of clustering iris samples by K-means clustering, including source code, sample data and clustering results)