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Method for data classification by kernel density shape interpolation of clusters

机译:通过聚类的核密度形状插值进行数据分类的方法

摘要

A method for obtaining a shape interpolated representation of shapes of one or more clusters in an image of a dataset that has been clustered comprises generating a density estimate value of each grid point of a set of grid points sampled from the image at a specified resolution for each cluster in the image using a kernel density function; evaluating the density estimate value of each grid point for each cluster to identify a maximum density estimate value of each grid point and a cluster associated with the maximum density estimate value of each grid point; and adding each grid point for which the maximum density estimate value exceeds a specified threshold to the cluster associated with the maximum density estimate value for the grid point to form a shape interpolated representation of the one or more clusters.
机译:一种用于获得已聚类的数据集的图像中一个或多个聚类的形状的形状插值表示的方法,该方法包括生成以指定分辨率从图像采样的一组网格点的每个网格点的密度估计值,用于图像中的每个簇都使用核密度函数;评估每个簇的每个网格点的密度估计值,以识别每个网格点的最大密度估计值和与每个网格点的最大密度估计值相关联的簇;将最大密度估计值超过指定阈值的每个网格点添加到与该网格点的最大密度估计值相关联的群集中,以形成一个或多个群集的形状插值表示。

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