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Clustering

于 2019-05-16 发布 文件大小:26KB
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  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.)

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