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Description: matlab programming for clustering pam , k-means , dbscan , optics for image segmentation
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Size: 21504 |
Author: Ben hassen Amer |
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Description: 当把一类对象划分到k个不同的类中时，主要的目标是找到类。PAM实现了将不同类别的数据自动划分到不同的类中-When partitioning a set of objects into k clusters, the main objective is to find clusters. The algorithm used in the program PAM is based on the search for k representative objects among the objects of the data set.
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Size: 11264 |
Author: 林文 |
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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.
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Size: 258048 |
Author: tra ba huy |
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Description: Ultra-Wideband
Communications
Systems
Multiband OFDM Approach
W. Pam Siriwongpairat
Meteor Communications Corporation
K. J. Ray Liu
University of Maryland
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Author: vahid |
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Description: PAM（Partitioning Around Medoid,围绕中心点的划分）算法是是划分算法中一种很重要的算法，有时也称为k-中心点算法，是指用中心点来代表一个簇。PAM算法最早由Kaufman和Rousseevw提出，Medoid的意思就是位于中心位置的对象。PAM算法的目的是对n个数据对象给出k个划分。PAM算法的基本思想：PAM算法的目的是对成员集合D中的N个数据对象给出k个划分，形成k个簇，在每个簇中随机选取1个成员设置为中心点，然后在每一步中，对输入数据集中目前还不是中心点的成员根据其与中心点的相异度或者距离进行逐个比较，看是否可能成为中心点。用簇中的非中心点到簇的中心点的所有距离之和来度量聚类效果，其中成员总是被分配到离自身最近的簇中，以此来提高聚类的质量。-PAM (Partitioning Around Medoid Around the division of the center,) algorithm is a kind of partition algorithm is very important algorithm, and sometimes also called k-center algorithm, it is to point to in the center to represent a cluster. The earliest PAM algorithm by Kaufman and Rousseevw puts forward, Medoid mean is at the center of the location of the object. PAM algorithm for the purpose of n data object is given k division. PAM algorithm to the basic idea of the: PAM algorithm for the purpose of members set D is the N data object given k division, forming k cluster, each cluster in selected at random from a members set to center, then at each step, the focus of the input data is not a member of the center according to the center YiDu or phase with each distance is, look to whether can be centered. Use cluster in the center point to the center of the cluster of the sum of all the distance to measure the clustering effect, which is always assigned members from their recent cluste
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Size: 2048 |
Author: 赵元 |
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Description: k中心点算法，也就是PAM算法。是数据挖掘中聚类分析的一种手段，用途广泛。-k center algorithm, i.e. PAM algorithm. Data mining is a means of cluster analysis, and versatile.
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Size: 2048 |
Author: 尚云 |
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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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Size: 37888 |
Author: CCGC
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Description: k-medoids with matlab is very strong
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Size: 863232 |
Author: akbari1368
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