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K均值
- 本程序通过k均值算法对两类进行分类。通过任意选择初始点，由k均值很快找到两类的中心点-the procedure k means algorithm to classify two types. Through arbitrary choice initial point, k Mean quickly found two focal point
K－均值聚类算法C++编程
- K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, inc
dataMining.rar
- 数据挖掘的软件，集成了关联规则、k-均值聚类、模糊聚类、k-中心点聚类四种算法,software of data mining
KMeansJava
- 利用Java实现的K-均值算法，K-Mean 分群法是一种分割式分群方法，其主要目标是要在大量高纬的资料点中找出 具有代表性的资料点；这些资料点可以称为群中心，代表点；然后再根据这些群中心，进行后续的处理，可用于数据挖掘中的聚类分析-Java implementation using K-means algorithm, K-Mean grouping method is a fragmented grouping method, whose main goal is to a large nu
KMeansClusterConsole
- K均值聚类算法，取前k个点为初始聚类中心，然后进行迭代聚类-K means clustering algorithm, fetch the first k points as initial cluster centers, then the iterative clustering
matlab_MRF
- k-均值算法的源码很有用，希望有帮助，请多多指点-k-means algorithm is useful source of hope that has helped, please instruct it
K
- K均值算法-分类器-有效抑制边缘点影响-简单有效-K-means algorithm- Classifier- effectively inhibiting the impact of edge points- simple and effective
OpencvdividedrandomlydistributepointK-meansalgorit
- 利用opencv编写的一段划分随机分布点集的K均值算法的程序代码-Opencv prepared to use a section of divided randomly distributed point set of K-means algorithm code
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
kmeans
- java k均值源码，实现了k-means的算法，并给出界面显示。实例中通过二维空间中的点进行聚类。-java k-means algorithm, display the cluster result on the two demension.
Kmeans
- 对已存入txt文件中的样本点就行K均值聚类，可输出质心和各个类包含的样本点-Txt files that have been deposited in the sample points on the line K means clustering, centroid, and each output class contains the sample points
k_means
- k均值聚类算法，可以完成k均值聚类运算。实现几个点的聚类-k means clustering algorithm, k means clustering operation can be completed. Clustering to achieve several points
k-means
- 用于实现二维点的k均值分类的功能。输入n个数以及要求分成的类别数，将输出最终的分类结果，并且得到代价。-Used to implement k-means classification of two-dimensional point of the function. N number of input and request the number of categories into the output end of the classification results, and get a
k_means
- K均值聚类，输入聚类前的点集以及阈值，输出最后各个分类的中心点-K-means clustering, the input point set before clustering and threshold, output the final classification of the center of each
K-average(N-dimension)
- K均值聚类算法实现有二维的聚类扩展到任意维样本点的聚类，代码中附加了详细的原理性说明，还有相关例子提示，效果不错-K-means clustering algorithm to achieve a two-dimensional clustering extends to any dimension of the cluster sample points, the code attached to the principle of detailed instructions, and tips
K-MEANS-N
- K均值聚类算法实现有二维的聚类扩展到任意维样本点的聚类.-K-means clustering algorithm to achieve a two-dimensional clustering extends to any dimension of the cluster sample points.
k-means
- c++实现k均值源码，实现了k-means的算法，并给出界面显示。实例中通过二维空间中的点进行聚类-c++ k-means algorithm, display the cluster result on the two demension
kmeans
- 基于k均值的无监督聚类算法，输出有各个样本的类别标签，目标函数在每次迭代后的值，聚类中心以及聚类区间。内有测试数据，点击 test.m 可以完美运行。(The unsupervised clustering algorithm based on K means outputs the class labels of each sample, the value of the target function after each iteration, the clustering center a
聚类分析
- 聚类分析算法 k均值算法 对地图上的点进行聚类事例(Clustering analysis algorithm k mean algorithm for clustering of points on maps)
kmeans
- 利用k均值聚类算法对数据进行聚类分析（数据点通过随机生成）(Using k-means clustering algorithm to cluster data (data points are generated randomly))