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Search - K-CLUSTER - List
[
Applications
]
k-average
DL : 0
k平均聚类算法,实现聚类的图形显示-k average clustering algorithm to achieve the cluster graphics
Update
: 2025-02-17
Size
: 50kb
Publisher
:
齐玉祥
[
AI-NN-PR
]
K-Means动态聚类算法源程序
DL : 0
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.
Update
: 2025-02-17
Size
: 30kb
Publisher
:
刘思
[
AI-NN-PR
]
以K-均值聚类结果为初始解的模拟退火聚类
DL : 0
由于K-均值聚类算法局部最优的特点,而模拟退火算法理论上具有全局最优的特点。因此,用模拟退火算法对聚类进行了改进。20组聚类仿真表明,平均每次对K结果值改进8次左右,效果显著。下一步工作:实际上在高温区随机生成邻域是个组合爆炸问题(见本人上载软件‘k-均值聚类算法’所述),高温跳出局部解的概率几乎为0,因此正考虑采用凸包约束进行模拟聚类,相关工作正在进行。很快将奉献给各位朋友。-as K-means clustering algorithm for optimal local characteristics, and simulated annealing algorithm theory with the characteristics of the global optimum. Thus, simulated annealing algorithm for clustering improvements. Cluster Group of 20 simulations show that the average value of K results improved about eight times, the results are obvious. The next step : In fact, in high temperature generated random neighborhood is a combination of explosives (see my software on the 'k-means clustering algorithm' mentioned above), high-temperature solution of partial out almost zero probability, it is considering the use of convex hull bound for simulation cluster, the work under way . Soon dedication to the ladies.
Update
: 2025-02-17
Size
: 5kb
Publisher
:
韩磊
[
Graph Recognize
]
K-means算法源码
DL : 0
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 using the K-means algorithm. Output is directed to the screen. Usage for KMEANS is: KMEANS SOURCEFILE <enter> The format of the source file is: NPat - Number of patterns (int) SizeVect - Size of vector (int) NClust - Number of cluster centers(int) vect[1,1] ... vect[1,SizeVect] - vector 1 (real) vect[2,1] ... vect[2,SizeVect] - vector 2 (real) . . . . . . vect[NPat,1] ... vect[NClust,SizeVector] - vector N (real) To compile: ICC KMEANS.CPP <enter> -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 using the K-means algorithm. Output is directed to the screen. Usage for KMEANS is: KMEANS SOURCEFILE <enter> The format of the source file is: NPat- Number of patterns (int) SizeVect- Size of vector (int) NClust- Number of cluster centers(int) vect[1,1] ... vect[1,SizeVect]- vector 1 (real) vect[2,1] ... vect[2,SizeVect]- vector 2 (real) . . . . . . vect[NPat,1] ... vect[NClust,SizeVector]- vector N (real) To compile: ICC KMEANS.CPP <enter>
Update
: 2025-02-17
Size
: 3kb
Publisher
:
li
[
Special Effects
]
将维对分和K均值算法分割图像
DL : 0
利用聚类算法分割图像,将维对分法只可将图像分为2部分,可以作为二值化的代码,K-均值法可将图像分为任意多部分。程序直接采用R、G、B三色作为特征参数,聚类中心为随机值,当然也可以采用其他参数,程序编译为EXE文件后速度还可以接受,但尚有改进的余地,那位高手有空修改的话,请给我也发份代码。-clustering algorithm using image segmentation, Victoria right method can only image is divided into two parts, the two values can be used as the source, K-means algorithm can be divided into images of arbitrary multi-part. Procedures used directly in R, G, B color as the characteristic parameters for the cluster center random value, of course, can also be used for other parameters, procedures EXE compiler to speed document acceptable, but there is still room for improvement, but the master of the time change, then please give me also made in the code.
Update
: 2025-02-17
Size
: 49kb
Publisher
:
pbt
[
Mathimatics-Numerical algorithms
]
K-MeansCluster
DL : 0
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.
Update
: 2025-02-17
Size
: 29kb
Publisher
:
Owen
[
matlab
]
K-Mean1
DL : 0
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)
Update
: 2025-02-17
Size
: 119kb
Publisher
:
[
CSharp
]
K-Meansshuo
DL : 0
C语言文件,关于求聚类分析的问题,谁有相同的问题可凉右-C language paper for cluster analysis on the issue, who has the same problem right cooler
Update
: 2025-02-17
Size
: 91kb
Publisher
:
wang
[
Special Effects
]
Kmeans.Cluster.using.Guide
DL : 0
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.
Update
: 2025-02-17
Size
: 111kb
Publisher
:
mecal
[
MPI
]
K-meanCluster
DL : 0
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.
Update
: 2025-02-17
Size
: 2kb
Publisher
:
yangdi
[
Other
]
KMEANS
DL : 0
k聚类免疫 算法的matlab仿真程序源码-k cluster immune algorithm matlab simulation program source
Update
: 2025-02-17
Size
: 29kb
Publisher
:
xixi
[
AI-NN-PR
]
cluster-1.37.tar
DL : 0
聚类算法包,包括K-Means,性能好,有例程-Clustering algorithm package, including K-Means, a good performance, there are routines
Update
: 2025-02-17
Size
: 947kb
Publisher
:
haibo zheng
[
AI-NN-PR
]
k-means
DL : 0
数据挖掘中的k均值算法,应该属于聚类分析的,c语言版。-Data Mining k-means algorithm, should belong to cluster analysis, c language version.
Update
: 2025-02-17
Size
: 2kb
Publisher
:
师帅
[
Editor
]
fuzzy-c-cluster
DL : 0
这是关于模糊K聚类的pdf文件,供大家学习-This is on the K fuzzy clustering pdf document for everyone to learn
Update
: 2025-02-17
Size
: 70kb
Publisher
:
zhs
[
AI-NN-PR
]
k-means
DL : 0
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,
Update
: 2025-02-17
Size
: 6kb
Publisher
:
tangkezong
[
Windows Develop
]
k_Mean
DL : 0
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
Update
: 2025-02-17
Size
: 21kb
Publisher
:
谢天培
[
Mathimatics-Numerical algorithms
]
Cluster
DL : 0
一个利用KDD1999数据集而完成的改进K-means聚类算法的实现.-A use of data sets KDD1999 completed to improve the K-means clustering algorithm.
Update
: 2025-02-17
Size
: 1004kb
Publisher
:
seaside
[
AI-NN-PR
]
cluster-2.9
DL : 0
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)
Update
: 2025-02-17
Size
: 55kb
Publisher
:
wangyexin
[
Special Effects
]
K
DL : 0
K-均值聚类算法,对数据进行聚类分析,可用于提取关键帧等。用matlab实现-K-means clustering algorithm, cluster analysis of data that can be used, such as key frame extraction. Using matlab to achieve
Update
: 2025-02-17
Size
: 120kb
Publisher
:
zhengmin
[
AI-NN-PR
]
k-centers
DL : 0
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
Update
: 2025-02-17
Size
: 16kb
Publisher
:
puguji
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