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Agglomerative hierarchical clustering of continuous variables based on mutual information

机译:基于互信息的连续变量的聚集层次聚类

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摘要

In order to study interdependencies among continuous variables in the framework of a data analysis problem, an agglomerative hierarchical clustering of the set of variables is performed. The similarity measure used within the clustering algorithm is based on the notion of mutual information. Recent results on the estimation of this measure of stochastic dependence are presented and the behavior of the clustering algorithm is studied on several artificial problems, i.e., which "structure" is known.
机译:为了在数据分析问题的框架中研究连续变量之间的相互依赖性,对变量集进行了聚集的层次聚类。聚类算法中使用的相似性度量基于互信息的概念。给出了估计这种随机依赖量度的最新结果,并且研究了聚类算法的行为以解决一些人工问题,即“结构”是已知的。

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