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kmean
- k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。-k-means algorithm process as follows: First of all, the object data from the n choose k
k_means
- 常用的k平均聚类 用于聚类分析 代码实现适用的是matlab-k means cluster applied to cluster analysis coded by matlab
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
KMEANS
- 实现k均值聚类算，输出聚类中心和聚类后的分组结果-To achieve k-means clustering calculation, the output cluster centers and cluster grouping of the results of post-
kMeansCluster-Code-For-matlab.m
- The code for k-means cluster in matlab. It works well in matlab.
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
k-means-clustering
- 用C语言程序通过先随机选取K个对象作为初始的聚类中心。然后计算每个对象与各个种子聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心。聚类中心以及分配给它们的对象就代表一个聚类。一旦全部对象都被分配了，每个聚类的聚类中心会根据聚类中现有的对象被重新计算。-C Programming Language by first randomly selected the K object as initial cluster centers. And then calculate the distan
K-means
- 修改参数可以实现任意样本总数，任意维数，任意聚类中心个数的K-Means Algorithm-The modify parameters can achieve any total number of samples, any dimension, any number of cluster centers K-Means Algorithm
KMEANS
- This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers u
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)
src
- k-means 算法接受参数 k ；然后将事先输入的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。-k-means algorithm accepts parameters k n and the previously input data is divided into k-clustering objects in order to make
Em
- 使用k均值算法计算聚类的重心，并用EM算法计算各聚类的参数-Using k-means clustering algorithm to calculate the center of gravity, and using EM algorithm to calculate the parameters of each cluster
KMeans
- K-均值聚类算法，属于无监督机器学习算法，发现给定数据集的k个簇的算法。 首先，随机确定k个初始点作为质心，然后将数据集中的每个点分配到一个簇中，为每个点找距其最近的质心， 将其分配给该质心对应的簇，更新每一个簇的质心，直到质心不在变化。 K-均值聚类算法一个优点是k是用户自定义的参数，用户并不知道是否好，与此同时，K-均值算法收敛但是聚类效果差， 由于算法收敛到了局部最小值，而非全局最小值。 K-均值聚类算法的一个变形是二分K-均值聚类算法，该算法首先将所有点作为一个簇，然
k-means
- This k means algorithm for clustering. Each cluster contains a center point called centroid. the algorithm converges early. this is a commonly used clustering algorithm.-This is k means algorithm for clustering. Each cluster contains a center point c
k-means-cluster
- 运用k均值的方法按照一定的规则将离散的数据进行聚类处理-Using k-means method in accordance with certain rules discrete data clustering