Description: 实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or) Platform: |
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Author:阿兜 |
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Description: 中心点漂移是一种非监督聚类算法（与k-means算法相似，但应用范围更广些),可用于图像分割，基于Matlab实现的源码。
MedoidShift is a unsupervised clustering algorithm(similar to k-means algorithm, but can be used in border application fields), can be used for image segmentation. Included is the Matlab implementation source code.-Center drift is a non-supervised clustering algorithm (k-means algorithm with the similar, but more a wider range of applications), can be used for image segmentation, based on the realization of the source Matlab. MedoidShift is a unsupervised clustering algorithm (similar to k-means algorithm, but can be used in border application fields), can be used for image segmentation. Included is the Matlab implementation source code. Platform: |
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Author:陈明 |
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Description: matlab编写的纹理图像分割 gussian滤波后k-means聚类 并将不同区域用线条表示出来 除m程序还包括测试图片、pdf文件、ppt文件、doc文档-matlab prepared texture image segmentation gussian filtered k-means clustering in different regions and lines that come out with the exception of m program also includes test pictures, pdf file, ppt file, doc file Platform: |
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Author:程雪娇 |
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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 ) Platform: |
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Author:快快 |
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Description: 在matlab下的k-均值聚类进行图像分类分割处理-In matlab under the k-means clustering for image classification be dealt with separately Platform: |
Size: 1024 |
Author:zhang |
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Description: 图像分割算法之间的比较，比较前沿的放发，利用了kmeans和模糊K均值-Between the image segmentation algorithm compares the release cutting-edge hair, use a K-means and fuzzy kmeans Platform: |
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Author:小牛 |
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Description: CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported.-CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported. Platform: |
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Author:tra ba huy |
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Description: 快速K均值聚类图像分割算法源代码，能很好的实现图像的分割处理-Fast K-means clustering algorithm for image segmentation source code, can achieve very good to deal with image segmentation Platform: |
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Author:zhangyun |
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Description: In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to convert a given gray level MR image in to a color space image and then separate the position of tumor objects from other items of an MR image by using c means clustering
And histogram clustering .Experiments demonstrates that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
-In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to convert a given gray level MR image in to a color space image and then separate the position of tumor objects from other items of an MR image by using c means clustering
And histogram clustering .Experiments demonstrates that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.
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Author:pramod |
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Description: 聚类方法中的K-means实现，用matlab语言实现的聚类-Clustering of K-means implementation of the cluster with matlab language Platform: |
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Author:收到回复 |
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Description: 在试验中编写程序实现了K均值聚类算法,K均值聚类的原理是：在训练样本中找到C个聚类中心，每个聚类中心代表一个类的中心。然后将样本归类到与其最近的聚类中心的那一类。 C的选择是通过先验知识或经验选取的。聚类中心是通过算法迭代求得的。-In the test preparation process to achieve a K means clustering algorithm, K means clustering principle is: in the training samples to find C a cluster center, each cluster center represents a kind of center. Then the samples are classified to the nearest cluster center with the type. C' s choice is a priori knowledge or experience through the selected. Cluster center is obtained through the iterative algorithm. Platform: |
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Author:王瑶 |
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Description: 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 observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.) Platform: |
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Author:invoker`Z |
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Description: 通过比较自编MATLAB 的k-means 算法程序和SPSS 中自带的k-means聚类工具，对两个数据集聚类，并分析了聚类结果。(By comparing the k-means algorithm program of self-compiled MATLAB with the K-means clustering tool of SPSS, two data sets are clustered and the clustering results are analyzed.) Platform: |
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Author:yty1018 |
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Search - k-means clustering with matlab