Description: ClustanGraphics聚类分析工具。提供了11种聚类算法。
Single Linkage (or Minimum Method, Nearest Neighbor)
Complete Linkage (or Maximum Method, Furthest Neighbor)
Average Linkage (UPGMA)
Weighted Average Linkage (WPGMA)
Mean Proximity
Centroid (UPGMC)
Median (WPGMC)
Increase in Sum of Squares (Ward s Method)
Sum of Squares
Flexible (ß space distortion parameter)
Density (or k-linkage, density-seeking mode analysis) -ClustanGraphics clustering analysis tools. Provides 11 kinds of clustering algorithms. Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean ProximityCentroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of SquaresFlexible (? space distortion parameter) Density (or k-linkage, density-seeking mode analysis) Platform: |
Size: 56320 |
Author:wangyexin |
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Description: 基于Pythoon的数值聚类分类算法，基于Python的三维立体点的空间最近邻分类(This example shows the effect of imposing a connectivity graph to capture local structure in the data. The graph is simply the graph of 20 nearest neighbors.
Two consequences of imposing a connectivity can be seen. First clustering
with a connectivity matrix is much faster.
Second, when using a connectivity matrix, average and complete linkage are
unstable and tend to create a few clusters that grow very quickly. Indeed,
average and complete linkage fight this percolation behavior by considering all the distances between two clusters when merging them. The connectivity graph breaks this mechanism. This effect is more pronounced for very
sparse graphs (try decreasing the number of neighbors in
kneighbors_graph) and with complete linkage. In particular, having a very small number of neighbors in the graph, imposes a geometry that is close to that of single linkage, which is well known to have this
percolation instability.) Platform: |
Size: 10240 |
Author:Merichiee |
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Description: 1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。
2) 使用C-Means算法对所有数据点进行聚类。C=3。
任务2（必做）：
使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。
任务3（全做）：
1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。
2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.) Platform: |
Size: 26624 |
Author:小鹏鹏123 |
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Search - single linkage clustering