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Visualizing high density clusters in multidimensional data using optimized star coordinates

机译:使用优化的星形坐标可视化多维数据中的高密度簇

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Multidimensional multivariate data have been studied in different areas for quite some time. Commonly, the analysis goal is not to look into individual records but to understand the distribution of the records at large and to find clusters of records that exhibit correlations between dimensions or variables. We propose a visualization method that operates on density rather than individual records. To not restrict our search for clusters, we compute density in the given multidimensional space. Clusters are formed by areas of high density. We present an approach that automatically computes a hierarchical tree of high density clusters. For visualization purposes, we propose a method to project the multidimensional clusters to a 2D or 3D layout. The projection method uses an optimized star coordinates layout. The optimization procedure minimizes the overlap of projected clusters and maximally maintains the cluster shapes, compactness, and distribution. The star coordinate visualization allows for an interactive analysis of the distribution of clusters and comprehension of the relations between clusters and the original dimensions. Clusters are being visualized using nested sequences of density level sets leading to a quantitative understanding of information content, patterns, and relationships.
机译:多维多元数据已在不同领域中研究了一段时间。通常,分析的目标不是查看单个记录,而是了解整个记录的分布,并找到在维度或变量之间具有相关性的记录簇。我们提出了一种可视化方法,该方法可根据密度而不是单个记录进行操作。为了不限制对聚类的搜索,我们在给定的多维空间中计算密度。簇由高密度区域形成。我们提出了一种自动计算高密度簇的层次树的方法。出于可视化目的,我们提出了一种将多维群集投影到2D或3D布局的方法。投影方法使用优化的星形坐标布局。优化过程将投影群集的重叠最小化,并最大程度地保持群集的形状,紧凑性和分布。星形坐标可视化允许对簇的分布进行交互分析,并理解簇与原始尺寸之间的关系。正在使用密度级别集的嵌套序列对集群进行可视化,从而导致对信息内容,模式和关系的定量理解。

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