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Description: 图像集群（Image Clustering）
(1)图像读入，显示图像所在路径；
(2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群；
(3)运行后，在原图像上显示集群灰度图；
(4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) read into the images, Images show host path; (2) use of imgcluster function for image clusters, After the number of clusters chosen for image clusters; (3) After the operation, in the original image displayed on the gray level clusters; (4) To show that the various clusters, [Show Open Clustering Image -- new windows, showed that the cluster type based on the maximum of regional painting. Clusters of non-current range, it shows that the intensity of 255 black. Users can click on View All button next cluster map.
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Size: 114137 |
Author: mecal |
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Description: 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 using the K-means algorithm. Output is
directed to the screen.-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 using the K-means algorithm. Output is directed to the screen.
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Size: 270336 |
Author: 赵丁香 |
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Description: 图像集群（Image Clustering）
(1)图像读入，显示图像所在路径；
(2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群；
(3)运行后，在原图像上显示集群灰度图；
(4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) read into the images, Images show host path; (2) use of imgcluster function for image clusters, After the number of clusters chosen for image clusters; (3) After the operation, in the original image displayed on the gray level clusters; (4) To show that the various clusters, [Show Open Clustering Image-- new windows, showed that the cluster type based on the maximum of regional painting. Clusters of non-current range, it shows that the intensity of 255 black. Users can click on View All button next cluster map.
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Size: 113664 |
Author: mecal |
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Description: 聚类分析中一个简单的聚类算法：Ｋ均值算法。-Cluster analysis of a simple clustering algorithm: K-means algorithm.
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Size: 240640 |
Author: 王桂芝 |
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Description: 在Visual C++.NET2003平台下实现聚类算法中的K-MEANS算法，对随机生成的点进行了聚类。使用单文档结构，并将聚类结果显示出来。-In the Visual C++. NET2003 platform clustering algorithm to achieve the K-MEANS algorithm, on randomly generated points in the cluster. Use a single document structure, and clustering results are displayed.
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Size: 63488 |
Author: 杨维斌 |
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Description: k聚类免疫
算法的matlab仿真程序源码-k cluster immune algorithm matlab simulation program source
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Size: 29696 |
Author: xixi |
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Description: kmeans算法实现
a simple k-means clustering routine.
returns the cluster labels of the data points in an array.-algorithm kmeans realize a simple k-means clustering routine.returns the cluster labels of the data points in an array.
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Size: 2048 |
Author: sisn |
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Description: JAVA实现的聚类中心的计算 算法比较简单 望多多指教 提宝贵意见-JAVA realize the cluster center is relatively simple algorithm for calculating the exhibitions look to the valuable advice
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Size: 2048 |
Author: sunny |
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Description: KMEANS Trains a k means cluster model.CENTRES = KMEANS(CENTRES, DATA, OPTIONS) uses the batch K-means
algorithm to set the centres of a cluster model. The matrix DATA
represents the data which is being clustered, with each row
corresponding to a vector. The sum of squares error function is used.
The point at which a local minimum is achieved is returned as
CENTRES.
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Size: 2048 |
Author: 西晃云 |
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Description: K-means算法，聚类分析中的一个重要的算法，用于分类-K-means algorithm, cluster analysis is an important algorithm for classification
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Size: 30720 |
Author: 陨石 |
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Description: K-MEANS算法
输入：聚类个数k，以及包含 n个数据对象的数据库。
输出：满足方差最小标准的k个聚类。
处理流程：
（1） 从 n个数据对象任意选择 k 个对象作为初始聚类中心；
（2） 循环（3）到（4）直到每个聚类不再发生变化为止
（3） 根据每个聚类对象的均值（中心对象），计算每个对象与这些中心对象的距离；并根据最小距离重新对相应对象进行划分；
（4） 重新计算每个（有变化）聚类的均值（中心对象）-K-MEANS algorithm Input: cluster number k, and contains n data object database. Output: the minimum standards to meet the variance k-clustering. Deal flow: (1) a data object from the n choose k object as initial cluster centers (2) cycle (3) to (4) until a change in each cluster is no longer so far (3) according to each Clustering objects mean (central object), calculated for each object with these centers to object distance and in accordance with a minimum distance between a re-division of the corresponding object (4) re-calculated for each (change) clustering of the mean (central object )
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Size: 3072 |
Author: 快快 |
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Description: 实现一个超级聚类程序,具有很好的参考价值.-The realization of a super-cluster procedure, with a very good reference value.
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Size: 27648 |
Author: 李德志 |
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Description: 聚类分析：K-Means动态聚类算法的源程序-Cluster analysis: K-Means clustering algorithm dynamic source
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Size: 29696 |
Author: wf |
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Description: 余弦距离进行聚类分析，余弦距离kmeans进行聚类分析，-Cluster analysis cosine distance, cosine distance from the cluster analysis kmeans,
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Size: 13312 |
Author: 刘明 |
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Description: clustering ---uses a kmeans method-clustering---uses a kmeans method
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Size: 4096 |
Author: ankit |
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Description: K-means is a clustering algorithm
In the beginning we determine number of cluster K and we assume the centroid or center of these clusters. We can take any random objects as the initial centroids or the first K objects in sequence can also serve as the initial centroids.
There are two function for that the kMeanCluster and distMatrix
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Size: 2048 |
Author: Luis |
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Description: 一种改进的均值聚类算法，能很好的利用与图像分割技术-k-means cluster
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Size: 1024 |
Author: 张先生 |
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Description: 基于MATLAB的kmeans聚类分析，包含数据和源代码，-MATLAB-based kmeans cluster analysis, including data and source code,
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Size: 4096 |
Author: 刘钊 |
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Description: K均值有效执行++多元数据的聚类算法。它已经表明，该算法具有的总群集内距离的期望值是日志（K）的竞争力的上限。此外，K -均值++通常远高于香草收敛K均值少。-An efficient implementation of the k-means++ algorithm for clustering multivariate data. It has been shown that this algorithm has an upper bound for the expected value of the total intra-cluster distance which is log(k) competitive. Additionally, k-means++ usually converges in far fewer than vanilla k-means.
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Size: 2048 |
Author: driiawrl |
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Description: 基于java语言实现的kmeans聚类算法，还是很靠谱的(kmeans cluster realize based on java)
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Size: 1024 |
Author: heimohe
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