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Description: 一个完整的以类的形式的k-medoids算法-to a complete category in the form of k-medoids algorithm
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Size: 1436 |
Author: 小朱 |
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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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Size: 1024 |
Author: 阿兜 |
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Description: 应用傅里叶变换DFT，分析各种离散信号x(k)的频谱。离散周期信号可以展开成傅里叶级数，所以离散周期信号的频谱 是一个周期的周期性离散频谱，各谱线之间的间隔为 ，而且存在着谐波的关系。 -DFT application of the Fourier transform, analyze discrete signal x (k) of the spectrum. Discrete signal cycle can begin as Fourier series, the discrete signal spectrum is a cyclical cycle of discrete spectrum, the line between the interval, but there is a harmonic relationship.
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Size: 38912 |
Author: 殷开 |
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Description: 非监督分类,主要在遥感图象分类处理中有应用-unsupervised classification, mainly in remote sensing image classification application is processed
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Size: 179200 |
Author: hua |
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Description: RS(n,k)编解码程序，自己稍稍修改后应用于某工程DSP实现的RS编码的程序！-RS (n, k) codec procedures, their slightly modified for a project of RS DSP coding procedures!
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Size: 13312 |
Author: cdl |
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Description: K-均值算法图形演示程序，可以设定聚类个数，采用MFC编写，有完善的K-均值类，可以对多维数据进行K-均值处理。-K-means algorithm graphics demo program, the number of clusters can be set using MFC preparation, a comprehensive K-average category, multidimensional data on K-mean treatment.
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Size: 102400 |
Author: |
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Description: 本程序在对图像进行纹理分析（由于共发矩阵的方法效果很不好，本程序采用基于频率域的纹理分析算法）的基础上，获取图像不同区域的纹理特征，针对这些纹理特征，采用聚类（K-mean)的分类算法对图像进行区域划分！-procedures in the right image texture analysis (due to a total of hair matrix, the effect is very bad, the program uses a frequency domain based on the texture analysis algorithm), on the basis of access to different regions of the image texture features, these texture characteristics, using clustering (K-mean) the right image classification algorithm for a regional breakdown!
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Size: 303104 |
Author: 陈镇静 |
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Description: k均值聚类的c#版本，我从网上找到的c版本经改造而成-k-means clustering of the c# version, I found from the Internet version of the modified form c
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Size: 2048 |
Author: 张恒敢 |
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Description: 本代码主要对 K_means 算法用java语言实现 .对需要java kmeans同志很帮助! 并附有测试文件!-This code mainly K_means algorithm using java language. Java kmeans need is to help comrades! With a test file!
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Size: 29696 |
Author: liunengxian |
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Description: apriori算法的java代码，APRRORI算法使用频繁项性质的先验知识，逐层搜索迭代，用K-项集产生(K+1)-项集。APRRORI算法的一个显著特点是:利用APRIORI性质，压缩了频繁项集，提高了算法的效率。
-apriori algorithm java code, APRRORI algorithm uses the a priori nature of frequent itemsets knowledge, step by step iterative search using K-itemsets generated (K+ 1)- itemsets. APRRORI algorithm A significant feature is: the nature of the use of APRIORI, compression of the frequent itemsets, improve the efficiency of the algorithm.
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Size: 21504 |
Author: xinyuanwo |
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Description: K最近邻分类的代码，附有输入输出和程序使用说明。-K nearest neighbor classification code, with input and output and procedures for use.
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Size: 518144 |
Author: 胡芬芬 |
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Description: k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。
Matlab 源代码，以兰花数据集作为测试对象。-k-means algorithm to accept input k then n data object is divided into k-clustering in order to make available to the cluster to meet: the same objects in clustering high similarity and objects in different clustering the similarity smaller. Cluster similarity is the use of the clustering of objects by means of a
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Size: 3072 |
Author: 烈马 |
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Description: it is Also Kmeans Algorithm in java.
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Size: 54272 |
Author: Naeem Ullah |
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Description: 数据挖掘的软件，集成了关联规则、k-均值聚类、模糊聚类、k-中心点聚类四种算法-software of data mining
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Size: 148480 |
Author: lqinggui |
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Description: 图片格式由jpg转换成ppm, K. MIKOLAJCZYK
University of Oxford, OX1 3PJ, Oxford, United Kingdom-Jpg image format conversion to ppm, K. MIKOLAJCZYK University of Oxford, OX1 3PJ, Oxford, United Kingdom
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Size: 3072 |
Author: lrw |
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Description: 对k-medoids与qpso结合的算法进行改进。选择调用qpso优化，降低运行时间，提高算法的执行效率。-Pairs of k-medoids algorithm combined with the qpso improvements. Select call qpso optimization, reducing operating time and improve the efficiency of the implementation of the algorithm.
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Size: 2048 |
Author: 飞 |
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Description: k均值聚类+matlab 有详细的注释和图片-failed to translate
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Size: 418816 |
Author: whnmyt |
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Description: 聚类算法中的k-medoids算法，和 k-means 肯定是非常相似的。事实也确实如此，k-medoids 可以算是 k-means 的一个变种。k-medoids 和 k-means 不一样的地方在于中心点的选取，在 k-medoids 算法中，我们将从当前 cluster 中选取这样一个点——它到其他所有（当前 cluster 中的）点的距离之和最小——作为中心点。-Clustering algorithm k-medoids algorithm, and k-means is certainly very similar. The fact is, k-medoids can be regarded as a variant of k-means. k-medoids and k-means not the same place that the center of the selection, in the k-medoids algorithm, we will the current cluster in a point- its distance to all other (current cluster of) the point of and the minimum- as a central point.
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Size: 14336 |
Author: 赵小娟 |
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Description: k-medoids with matlab is very strong
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Size: 863232 |
Author: akbari1368
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Description: 基于k-medoids算法的改进算法，在第五代移动通信中D2D通信中的应用。(The improved algorithm based on k-medoids algorithm is applied to D2D communication in the fifth generation mobile communication.)
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Size: 1024 |
Author: winterisgone |
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