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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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Author: 刘思 |
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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)
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Author: 阿兜 |
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Description: 模式识别的经典算法之一，动态聚类的k均值算法，采用matlab进行编程，并对分类进行了画图分析。-the classic pattern recognition algorithms, dynamic clustering algorithm k mean using Matlab programming, as well as classification of the class analysis.
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Author: 也风 |
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Description: 数据挖掘常用的算法，包括id3，k均值，FCM，SVM,CART五个常用的算法，是用matlab编写的。-commonly used data mining algorithms, including id3, k mean, FCM, the SVM, CART five commonly used algorithm is the use of Matlab prepared.
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Author: 李冬宾 |
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Description: 这个是K-Means动态聚类算法的源程序，需要的请下，请多多指教，谢谢！-This is a K- Means clustering algorithm source, the need to please, please exhibitions, thank you!
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Size: 32768 |
Author: HaicoLi |
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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。-achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n objects assigned K to 000 category, making such objects within the similarity between the largest category of the similarity between the smallest.
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Author: yili |
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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: 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: 快速k-means算法，比matlab 自带的要快很多。-rapid k-means algorithm, compared to the Matlab bringing their much faster.
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Author: zhang chun xiao |
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Description: K近邻法的matlab程序，发现大家都在找它！-K-nearest neighbor method of Matlab procedures, I found that we all have to find it!
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Author: wang |
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Description: 基于纹理度量的图像分割，适用于遥感图像，利用到K-mean算法-texture measurement based on the image segmentation, applicable to remote sensing images, the use of K-mean algorithm
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Author: lichuan |
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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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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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Author: xxl |
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Description: k-均值算法 做的图象分割实例 （将图片像素聚成三类）-k-means algorithm to do the image segmentation example (poly-pixel pictures into three categories)
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Author: 姜波 |
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Description: K-MEAN:经典K均值算法,适用领域：语音识别，图像识别-K-MEAN: classical K-means algorithm, the application areas: speech recognition, image recognition
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Author: annie |
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Description: 各类聚类算法程序包，包含各种经典的聚类算法，例如：k-mean聚类等-Various types of clustering algorithm package, contains a variety of classic clustering algorithms, such as: k-mean clustering, etc.
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Author: 王朝霞 |
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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: 基于K-MEAN的图像分割,方便实用，对于图像处理的研究生很有参考价值的-watershed segmentation on matlab
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Author: jin |
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Description: K-means算法是很典型的基于距离的聚类算法，采用距离作为相似性的评价指标，即认为两个对象的距离越近，其相似度就越大(K-means algorithm is a typical distance based clustering algorithm. The distance is used as the evaluation index of similarity, that is, the closer the distance between the two objects, the greater the similarity)
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Author: venshow
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Description: k均值，数据已经有了，主要用于分类，美列都是一类数据，只用了其中一部分，数据是自己编的。(K mean, data already exists, mainly for classification, the United States column is a kind of data, only a part of the data is their own series.)
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Author: guanyu
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