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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_meansc_meansCluster
- 基于k均值、c均值等聚类算法,应用于数据挖掘-Based on the mean k, c means clustering algorithm, etc., used in data mining
k_means
- 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。一般都采用均方差作为标准测度函数. k个聚类具有以下特点:各聚类本身尽可能的紧凑,而各聚类之间尽可能的分开。-First, a data object from the n choose k objects as in
kmeans
- 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 accept parameter k Then will the n of prior input data object is divided into k clustering to make won clu
kmo
- k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the clu
Kmeans1
- 利用K-mean的方法对样本进行聚类,只需要输入训练样本的地址和所要聚类的个数就可以完成聚类-The method of using K- mean to clustering samples, only need to input the training sample address and the number of clustering,and then it can be completed by clustering
K-means-cluster
- k-means算法是一种动态聚类算法,基本原理如下[24]:首先预先定义分类数k,并随机或按一定的原则选取k个样品作为初始聚类中心;然后按照就近的原则将其余的样品进行归类,得出一个初始的分类方案,并计算各类别的均值来更新聚类中心;再根据新的聚类中心对样品进行重新分类,反复循环此过程,直到聚类中心收敛为止。-K- means algorithm is a dynamic clustering algorithm, the basic principle of [24] as follows: fi
kingjao
- 基于K均值的PSO聚类算法,计算多重分形非趋势波动分析matlab程序,均值便宜跟踪的示例。- K-means clustering algorithm based on the PSO, Calculation multifractal detrended fluctuation analysis matlab program, Example tracking mean cheap.
segmentation
- Segmentation with k means clustering and mean shift
kmean-juleisuanfa
- 一个kmean聚类算法的实现,一个很好的示例,仅供大家一起参考和学习-K achieve a mean clustering algorithm, a good example, for your reference and learning together
gangking_V3.2
- 基于K均值的PSO聚类算法,包含收发两个客户端的链路级通信程序,均值便宜跟踪的示例。- K-means clustering algorithm based on the PSO, Contains two clients receive link-level communications program, Example tracking mean cheap.
gengnou_v78
- 基于K均值的PSO聚类算法,均值便宜跟踪的示例,非归零型差分相位调制信号建模与仿真分析 。- K-means clustering algorithm based on the PSO, Example tracking mean cheap, NRZ type differential phase modulation signal modeling and simulation analysis.
fangyei_v51
- 基于K均值的PSO聚类算法,解耦,恢复原信号,最小均方误差(MMSE)的算法。- K-means clustering algorithm based on the PSO, Decoupling, restore the original signal, Minimum mean square error (MMSE) algorithm.
AIS
- 船舶AIS轨迹提取,作出船舶AIS轨迹图。在此基础上,实现基于空间相似距离的K均值轨迹聚类。算法可以自定义轨迹聚类初始轨迹或者由程序随机生成初始轨迹,采用迭代的方法直到确定最终的聚类中心。-The extraction of ship AIS trajectory, make ship AIS trajectories. On this basis, to achieve similar K mean trajectory clustering based on distance space.
kmeans (2)
- k mean code for clustering data
Speech Processing Analysis - MATLAB
- The number of states in GMM as the generative model of the frames is obtained using k-means algorithm. This also helps to initialize the mean vector and the covariance matrix of the individual state of the GMM. The training LPC frames collected fro
cskmeans
- k均值聚类 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。(kmeans cluster K means clustering algorithm accepts parameters K; N data object classification and the previously inpu
Rolling bear
- 采用PCA白化和K均值对轴承故障进行聚类分析(Clustering analysis of bearing failure by PCA whitening and K mean)
AP聚类
- AP聚类算法是基于数据点间的"信息传递"的一种聚类算法。与k-均值算法或k中心点算法不同,AP算法不需要在运行算法之前确定聚类的个数。(AP clustering algorithm is a kind of clustering algorithm based on "information transfer" between data points. Unlike the k- mean algorithm or the k center point
聚类算法
- 文件夹中主要有二维的K-means,gmm,mean-shift,三维的K-means聚类算法的程序,同时已经经过本人验证无误,可以成功运行,有问题的可以私下交流。(Folder mainly two-dimensional k-means, GMM, mean-shift, three-dimensional k-means clustering algorithm procedures, at the same time has been verified by myself, can be