Description: 我自己编写的分层聚类算法，类内采用最大距离，类间采用最小距离实现-myself prepared by the Hierarchical clustering algorithm, the largest category within distance between categories of use to achieve minimum distance Platform: |
Size: 1024 |
Author:张成 |
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Description: 一种高效的聚类算法给定要聚类的N的对象以及N*N的距离矩阵(或者是相似性矩阵), 层次式聚类方法的基本步骤(参看S.C. Johnson in 1967)如下:-An Efficient Algorithm for the cluster must be the object of N and N* N distance matrix (or similarity matrix), the hierarchical clustering method the basic steps (see S. C. Johnson in 1967), as follows : Platform: |
Size: 432128 |
Author:毛显锋 |
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Description: 层次聚类算法中的Cure算法，可以用于识别非球形的簇，解决了偏好球形和相似大小的问题，在处理孤立点上也更加健壮。-Hierarchical clustering algorithm of Cure algorithm can be used to identify non-spherical cluster, the preferred solution is similar to spherical and the size of the problem, in dealing with isolated points and more robust. Platform: |
Size: 6144 |
Author:刘嘉良 |
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Description: 在Visual C++下实现聚类分析在图像识别中的应用。其中包括模糊聚类，遗传算法聚类分析，层次聚类和动态聚类等算法。可以运行，并有分类的源文件。是不错的研究素材。-In Visual C++ Achieve clustering analysis in image recognition applications. Including fuzzy clustering, genetic algorithm for cluster analysis, hierarchical clustering and dynamic clustering algorithm, etc.. Can run, and the classification of the source file. Is a good study material. Platform: |
Size: 229376 |
Author:dd |
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Description: 用matlab实现聚类算法，注意是层次聚类和未知类别聚类算法！-Clustering algorithm using matlab implementation, pay attention to are hierarchical clustering and unknown type of clustering algorithm! Platform: |
Size: 16384 |
Author:张晓飞 |
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Description: Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".
Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in
multiple regression are linearly independent: if all the diagonal
elements of inverse correlation matrix of data are less than VIF-Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al- Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".
Namely, it searches cutoff where groups are independent. The techinque uses an econometric approach of verifying that variables in
multiple regression are linearly independent: if all the diagonal
elements of inverse correlation matrix of data are less than VIF Platform: |
Size: 2048 |
Author:tra ba huy |
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Description: 通过设计线性分类器；最小风险贝叶斯分类器；监督学习法分层聚类分析；K－L变换提取有效特征，设计支持向量机对给定样本进行有效分类并分析结果。-By designing a linear classifier minimum risk Bayes classifier supervised learning method hierarchical cluster analysis K-L transform to extract efficient features, designed to support vector machines for effective classification of a given sample and analyze the results. Platform: |
Size: 2599936 |
Author:于冰 |
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Description: BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, Birch only requires a single scan of the database. In addition, Birch is recognized[1] as the, "first clustering algorithm proposed in the database area to handle noise (data points that are not part of the underlying pattern) effectively".-BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, Birch only requires a single scan of the database. In addition, Birch is recognized[1] as the, "first clustering algorithm proposed in the database area to handle noise (data points that are not part of the underlying pattern) effectively". Platform: |
Size: 2048 |
Author:pepe |
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Description: 使用SPSS实施常用聚类算法：系统聚类法和谱系聚类法，可进行各种聚类方法结果的分析比较-SPSS implementation of the commonly used clustering algorithms: clustering method and the hierarchical clustering method, the analysis of the results of various clustering methods Platform: |
Size: 233472 |
Author:yirufang |
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Search - hierarchical cluster