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Description: MATLAB编写的一种模式识别里的重要的模糊聚类方法K-means算法的matlab程序.-MATLAB prepare a Pattern Recognition's important Fuzzy clustering methods K-means algorithm Matlab procedures.
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Author: zs |
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Description: K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n * k * m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.
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Size: 4072 |
Author: 韩磊 |
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Description: km2.m是matlab程序，test.txt是数据源
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Size: 920 |
Author: heatingequ@163.com |
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Description: K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.
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Author: 韩磊 |
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Description: 算法介绍
矩阵求逆在程序中很常见，主要应用于求Billboard矩阵。按照定义的计算方法乘法运算，严重影响了性能。在需要大量Billboard矩阵运算时，矩阵求逆的优化能极大提高性能。这里要介绍的矩阵求逆算法称为全选主元高斯-约旦法。
高斯-约旦法（全选主元）求逆的步骤如下：
首先，对于 k 从 0 到 n - 1 作如下几步：
从第 k 行、第 k 列开始的右下角子阵中选取绝对值最大的元素，并记住次元素所在的行号和列号，在通过行交换和列交换将它交换到主元素位置上。这一步称为全选主元。
m(k, k) = 1 / m(k, k)
m(k, j) = m(k, j) * m(k, k)，j = 0, 1, ..., n-1；j != k
m(i, j) = m(i, j) - m(i, k) * m(k, j)，i, j = 0, 1, ..., n-1；i, j != k
m(i, k) = -m(i, k) * m(k, k)，i = 0, 1, ..., n-1；i != k
最后，根据在全选主元过程中所记录的行、列交换的信息进行恢复，恢复的原则如下：在全选主元过程中，先交换的行（列）后进行恢复；原来的行（列）交换用列（行）交换来恢复。-algorithm introduced in the matrix inversion process is very common, which are mainly used for Billboard matrix. In accordance with the definition of the method of calculating multiplication, seriously affecting the performance. The need for a large number of Billboard matrix operations, matrix inversion optimization can significantly improve performance. Here we introduce the matrix inversion algorithm called full-elected PCA Gauss-Jordan and France. Gauss-Jordan and France (all elected PCA) inversion of the following steps : First, for k from 0 to n-1 for the following steps : from the first trip k, k started out the bottom right corner Subarray largest absolute selected elements, and element remember meeting the line and out, the adoption OK exchange and the exchange out of its exchange
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Author: 刘亮 |
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Description: MATLAB编写的一种模式识别里的重要的模糊聚类方法K-means算法的matlab程序.-MATLAB prepare a Pattern Recognition's important Fuzzy clustering methods K-means algorithm Matlab procedures.
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Size: 55296 |
Author: zs |
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Description: 模糊c均值,m值，误差限，迭代次数由用户自己输入，聚类效果好。-fuzzy c-means, m value, the error limits, the number of iterations user input, clustering effects.
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Size: 3072 |
Author: 杨韧洲 |
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Description: 关于k-means聚类算法的MATLAB实现-On the k-means clustering algorithm of MATLAB realize
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Author: chen |
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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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Author: 快快 |
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Description: K-nearest neighbors 搜索 聚类时经常使用的一种方法 国外网站转载- The following utilities are provided:
- Nearest neighbor
- K-Nearest neighbors
- Radius Search
They al supports N-dimensions and work on double, it is possible to choose if return the distances.
Here is a time comparison with a vectrized m-code:
N=1000000 number of reference points
Nq=100 number of query points
dim=3 dimension of points
k=3 number of neighbor
tic
[idc,dist]=BruteSearchMex(p ,qp , k ,k) MEX
toc
tic
[idc,dist]=knnsearch(qp,p,k) VECTORIZED M-CODE
toc
p=rand(N,dim)
qp=rand(Nq,dim)
Output:
Elapsed time is 0.962640 seconds.
Elapsed time is 18.813100 seconds.
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Author: Ming |
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Description: K-mean均值算法的matlab功能实现-K-means to achieve the matlab function
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Author: spinach |
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Description: K mean cluster matlab code
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Author: amin amini |
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Description: 将K均值算法用于图像分割,输入的是彩色图像,转换为灰度图像进行分割,输出结果为灰度图像.利用灰度做为特征对每个像素进行聚类,由于光照等原因,有时应该属于一个物体的像素,其灰度值也会有很大的差别,可能导致对该像素的聚类发生错误.在分割结果中,该物体表面会出现一些不同于其它像素的噪声点,因此,算法的最后,对结果进行一次中值滤波,以消除噪声,达到平滑图像的作用-The K means algorithm for image segmentation, the input is a color image, convert to grayscale image segmentation, the output of grayscale images. The use of gray as the characteristics of each pixel clustering, due to light and other reasons, and sometimes should belong to an object pixel, its gray value will also be very different, may lead to clustering of the pixel error has occurred. in the segmentation results, the surface, there would be different from other pixel noise points, so , the algorithm Finally, the results of a median filter to eliminate noise, to the role of smoothing the image
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Author: caoliang |
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Description: k-近邻算法 m文件
及其excel示范-this package invulved a m-file example of k-nearest neighbour and its corresponding excel file
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Size: 38912 |
Author: guoguozhong |
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Description: 该程序为matlab程序，共有三个文件，dataC.m为程序入口，实现功能对50组数据用k均值算法进行分类，再对40组数据用感知器算法训练，然后用训练得到的判别函数对剩下10组数据分类，最后与原始分类做差比较，若分类无误，则全显示为0.-Matlab program on the program, a total of three files dataC.m for program entry features 50 sets of data with k-means algorithm to classify 40 sets of data with the training of the perceptron algorithm, and then using the trained discriminant function The remaining 10 sets of data classification, and finally with the original classification poor comparison, if the classification is correct, then the whole show.
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Author: 阿书 |
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Description: Fast mex K-means clustering algorithm with possibility of K-mean++ initialization
(mex-interface modified from the original yael package https://gforge.inria.fr/projects/yael)
- Accept single/double precision input
- Support of BLAS/OpenMP for multi-core computation
Please run mexme_yael_kmeans.m to compile mex-files (be sure that mex -setup have been done at least one)
Run demo "test_yael_kmeans.m"-Fast mex K-means clustering algorithm with possibility of K-mean++ initialization
(mex-interface modified from the original yael package https://gforge.inria.fr/projects/yael)
- Accept single/double precision input
- Support of BLAS/OpenMP for multi-core computation
Please run mexme_yael_kmeans.m to compile mex-files (be sure that mex-setup have been done at least one)
Run demo "test_yael_kmeans.m"
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Author: zfhou |
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Description: 基于MATLAB的编写的K均值聚类实验多类算法，caculateCenter1.m是确定类的中心，主函数是main-Experimental multi-class algorithm based on MATLAB prepared by K-means clustering, caculateCenter1.m the center determines the class, the main function is the main
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Author: 徐阳 |
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Description: 一种k-means聚类算法的图像分割例程，很好用。里面包括主函数和子函数的m文件以及使用的图片。-One kind of k-means clustering image segmentation algorithm routines, very good use. Which includes the main functions and subroutines m documents and pictures used.
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Author: 杜静文 |
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Description: 欢迎使用和评述此工具箱，您的意见是对我们工作的支持。
此工具适合于不同有效性指标的性能比较，改进代码用于不同的应用问题等等。
(1) NCT的内容
NCT包括4个外部有效性指标和8种内部有效性指标，编制的程序文件"validity_Index.m"用于调用它们
(2) 主文件 "mainClusterValidationNC.m" 的内容
主文件设计为如何使用PAM聚类算法、如何使用有效性指标和方法来估计聚类个数。(Help file of Toolbox for estimating the number of clusters (NCT)
(Version 2.0)
Your comments are welcome at:
http://www.mathworks.com/matlabcentral/fileexchange/13916
E-mail: wangkjun@yahoo.com
(1) Contents of NCT
The NCT includes 4 External validity indices and 8 internal validity indices, and the sub-routine "validity_Index.m" is designed to use them.
This tool is suitable for the research work such as the performance comparison of different indices on estimation of the number of clusters, algorithm design by improving part codes of this toolbox.
(2) Contents of main file "mainClusterValidationNC.m"
It is designed to use validity indices to estimate the number of clusters (NC) for PAM and K-means clustering algorithms.)
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Author: CCGC
|
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Description: 基于K-means聚类分割算法，可以去掉背景。直接运行fenge.m文件即可，程序文件里给出了注释说明，可以根据需求修改参数和测试图像。
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Size: 28621 |
Author: htgao2019@foxmail.com |
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