Location:
Search - matlab K-mean
Search list
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.
Platform: |
Size: 30720 |
Author: 刘思 |
Hits:
Description: 基于划分的聚类分析算法k-means,主要用于数据挖掘领域.-Partition- based cluster analysis algorithm k-means, used mainly for data mining areas.
Platform: |
Size: 3072 |
Author: 许朝 |
Hits:
Description: 绝对好用的分割算法，用Ｍａｔｌａｂ编写，绝对无毒-absolutely useful segmentation algorithm, using Matlab preparation, absolute non-toxic
Platform: |
Size: 1024 |
Author: 吕鸿发 |
Hits:
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: |
Size: 1024 |
Author: 阿兜 |
Hits:
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.
Platform: |
Size: 2048 |
Author: 也风 |
Hits:
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.
Platform: |
Size: 18432 |
Author: 李冬宾 |
Hits:
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!
Platform: |
Size: 32768 |
Author: HaicoLi |
Hits:
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.
Platform: |
Size: 1024 |
Author: yili |
Hits:
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)
Platform: |
Size: 121856 |
Author: |
Hits:
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.
Platform: |
Size: 55296 |
Author: zs |
Hits:
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.
Platform: |
Size: 2048 |
Author: yangdi |
Hits:
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)
Platform: |
Size: 104448 |
Author: 姜波 |
Hits:
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,
Platform: |
Size: 6144 |
Author: tangkezong |
Hits:
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
Platform: |
Size: 70656 |
Author: annie |
Hits:
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)
Platform: |
Size: 1024 |
Author: 刘玉平 |
Hits:
Description: Image segmentation k mean clustering
Platform: |
Size: 2048 |
Author: truong minh sang |
Hits:
Description: K-mean均值算法的matlab功能实现-K-means to achieve the matlab function
Platform: |
Size: 55296 |
Author: spinach |
Hits:
Description: 基于K-MEANS的图像退色算法。平台为MATLAB6.5及以上。-Based on the K-MEANS algorithm image fade
Platform: |
Size: 191488 |
Author: 臧超 |
Hits:
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
Platform: |
Size: 34816 |
Author: jin |
Hits:
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)
Platform: |
Size: 13312 |
Author: venshow
|
Hits: