Diagonally Colorized iVAT Images for Labeled Data

Elizabeth D. Hathaway, R. Hathaway
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Abstract

The iVAT (improved Visual Assessment of cluster Tendency) image is a useful tool for assessing possible cluster structure in an unlabeled, numerical data set. If labeled data are available then it is sometimes helpful to determine how closely the (unlabeled) data clusters agree with the data partitioning based on the labels. In this note the DCiVAT (Diagonally Colorized iVAT) image is introduced for the case of labeled data. It incorporates all available data and label information into a single colorized iVAT image so that it is possible to visually assess the degree to which data clusters are aligned with label categories. The new approach is illustrated with several examples.
标记数据的对角线彩色iVAT图像
iVAT(改进的聚类趋势视觉评估)图像是在未标记的数值数据集中评估可能的聚类结构的有用工具。如果有标记的数据可用,那么确定(未标记的)数据集群与基于标签的数据分区的一致程度有时是有帮助的。在本文中,针对标记数据的情况,介绍了DCiVAT(对角线彩色iVAT)图像。它将所有可用的数据和标签信息合并到单个彩色iVAT图像中,以便可以直观地评估数据簇与标签类别对齐的程度。用几个例子说明了这种新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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