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cluster-2.9
- ClustanGraphics聚类分析工具。提供了11种聚类算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean Proximity Centroid (UPGMC)
k_means_cluster
- k均值聚类算法 ，c语言实现 了基于均值的聚类分析，同时增加了多维向量分析功能，使得聚类的收敛速度更快。-k means clustering algorithm, c language implemented based on the mean cluster analysis, while increasing the multi-dimensional vector analysis functions, making the convergence faster clustering.
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
color-image-segment-code
- 这是一个关于彩色图像分割的报告，内含源代码及处理结果等，图像分割采用K-mean聚类分割方法。-K-mean cluster segment
K-MEANS
- 均值计算方法源码实现：分群的方法，就改成是一个最佳化的問題，換句话說，我們要如何选取 c 个群聚以及相关的群中心，使得 E 的值为最小。 -Method of calculating the mean source implementation: clustering method, based on the best change is a problem, in other words, how do we choose c a center cluster and related g
mean-K-KPCA
- 通过核 K- 均值聚类的方法对语音帧进行聚类 , 由于聚类的中心能够很好地代表类内的特征, 用中心样本帧取代该类, 减少了核矩阵的维数, 然后再采用稀疏 KPCA方法对核矩阵进行特征提取。-Through the nuclear K-means clustering method for clustering of speech frames, the cluster center can be a good representative of the class characteristics
K-Mean-Clustering-Code-in-Matlab
- k 均值聚类算法 ，能有效的将数据分成k类 但是具有k参数难以确定的缺点。 -k-means algorithm can cluster data into K class but, the parameter K can not be selected easily.
kmean
- k mean for clustering in the matlab.cluster do in environment 3-dimontional.
K-mean
- 聚类分析的目标就是在相似的基础上收集数据来分类。聚类源于很多领域，包括数学，计算机科学，统计学，生物学和经济学。-Cluster analysis the goal is to collect data on the basis of similar classification. Cluster from many fields, including mathematics, computer science, statistics, biology and economics.
graph4.c.tar
- k mean clustering to cluster a hard of test
kmeans1
- kmeans ieee paper , A system for analyzing student’s results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. K-mean clustering algorithm for a
k_mean_uv
- 利用k—mean算法根据U、V进行聚类分块，最后完成图像分割，分割出得块用不同的灰度等级表示出来。-K-mean algorithm to cluster sub-blocks according to the U, V, to finalize the image segmentation, segmentation have blocks with different gray levels represented.
K-Mean
- Kmeans 聚类算法的实现 测试， 内部包含 Kmode选项-Implementation of Kmeans cluster algorithm and testing, internal options include Kmode
K-mean
- 聚类算法中的k-means算法，和k-medoids 肯定是非常相似的。k-medoids 和 k-means 不一样的地方在于中心点的选取，在 k-means 中，我们将中心点取为当前 cluster 中所有数据点的平均值。-Clustering algorithm k-means algorithm, and k-medoids certainly very similar. k-medoids and k-means not the same place that the center o
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.
k
- k mean algorithm implementation using random cluster centroid
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
Random-center---segma
- k mean cluster for radial basis function neural network
kmeans (2)
- k mean code for clustering data
K-means
- K-means聚类算法的matlab实现(k-means clustering is a method of vector quantization, originally from 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 obse