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Description: How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
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Size: 2004 |
Author: yangdi |
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Description: This directory contains code implementing the K-means algorithm. Source codemay be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANSprogram accepts input consisting of vectors and calculates the givennumber of cluster centers using the K-means algorithm. Output isdirected to the screen.
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Size: 30720 |
Author: 刘思 |
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Description: K-Means是聚类分析中重要的一种方法，此源码是K-Means聚类分析的C语言实现。-K- Means clustering analysis is the most important way, this source is K- Means clustering analysis of the C language.
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Size: 29696 |
Author: Owen |
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Description: 编写K-均值聚类算法程序，对下图所示数据进行聚类分析(选k=2)-prepare K-means clustering algorithm, the data shown in the chart below cluster analysis (EAC k = 2)
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Description: How the K-mean Cluster work
Step 1. Begin with a decision the value of k = number of clusters
Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following:
Take the first k training sample as single-element clusters
Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster.
Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample.
Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. -How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
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Size: 2048 |
Author: yangdi |
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Description: 用于遥感图像分类。其输入为几幅遥感图像，使用k-mean聚类方法对图像中的不同地形进行聚类分割-For remote sensing image classification. Their input for a number of remote sensing images, the use of k-mean clustering method to image the topography of the different cluster partition
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Size: 419840 |
Author: xxl |
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Description: 基于粗糙熵和K-均值聚类算法的图像分割 基于粗糙熵和K-均值聚类算法的图像分割-Based on Rough Entropy and K-means clustering algorithm for image segmentation based on rough entropy and K-means clustering algorithm for image segmentation
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Size: 257024 |
Author: 张三 |
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Description: 空间数据分析中最常用的是聚类分析,而K-MEANS算法是聚类分析中常用的,其主要思想是在给定的聚类数目下对多维(我做的是三维空间点)向量进行聚类,-Spatial data analysis is the most commonly used cluster analysis, while the K-MEANS algorithm is commonly used in cluster analysis, the main idea is to set the number of under the multi-dimensional clustering (I make the three-dimensional space-point) vector cluster,
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Size: 6144 |
Author: tangkezong |
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Description: 蛙蛙的中文文本聚类，主要采用k-means算法。wawa s text cluster using C#.-蛙蛙Chinese text clustering, the main use of k-means algorithm. wawa s text cluster using C#.
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Size: 16384 |
Author: 陈石 |
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Description: K聚类分析，通过使用欧式距离，K聚类方法显示聚类结果，用于分类-K cluster analysis, using Euclidean distance, K show the clustering results of clustering method for classification
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Size: 21504 |
Author: 谢天培 |
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Description: 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)
Median (WPGMC)
Increase in Sum of Squares (Ward s Method)
Sum of Squares
Flexible (ß space distortion parameter)
Density (or k-linkage, density-seeking mode analysis) -ClustanGraphics clustering analysis tools. Provides 11 kinds of clustering algorithms. Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean ProximityCentroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of SquaresFlexible (? space distortion parameter) Density (or k-linkage, density-seeking mode analysis)
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Author: wangyexin |
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Description: k均值聚类算法源码（matlab）
k均值聚类算法源码（matlab）-k-means clustering algorithm source code (matlab) k-means clustering algorithm source code (matlab)
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Size: 1024 |
Author: 刘玉平 |
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Description: 一种基于软划分方法的聚类方法——模糊k均值法聚类分析。-A division of methods based on soft clustering method- fuzzy k-means cluster analysis.
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Size: 1024 |
Author: wyl |
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Description: 不同于k均值聚类的k中心聚类，2007年SCIENCE文章Clustering by Passing Messages Between Data Points 中的方法-Unlike k-means clustering of the k cluster centers, in 2007 SCIENCE article, Clustering by Passing Messages Between Data Points of the Method
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Size: 16384 |
Author: puguji |
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Description: K-mean image segementation Choose cluster centers and point-cluster allocations to minimize error
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Size: 1024 |
Author: Umair |
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Description: K mean cluster matlab code
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Size: 1024 |
Author: amin amini |
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Description: k最邻近算法，经典的分类算法，绝对有帮助-k-nearest neighbour algorithm,it is a classical algorithm for text cluster
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Size: 17408 |
Author: freesunshine |
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Description: 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.
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Size: 8192 |
Author: 徐廷 |
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Description: 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。
-k-means algorithm to accept input k then n-k of data objects into a cluster in order to make the cluster available to meet: the object of the same cluster in the high similarity the similarity of different objects in clusters smaller. Cluster similarity is the use of the mean of each cluster obtained by objects in a " central object" (center of gravity) to be calculated.
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Size: 929792 |
Author: Chenguang |
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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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