Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts

Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts

Blog Article

Two-dimensional space embeddings such as Multi-Dimensional Scaling (MDS) are a popular means to gain insight into high-dimensional data relationships.However, in all but the simplest cases these embeddings suffer from significant distortions, which can lead to misinterpretations of the high-dimensional data.These distortions occur both at the global inter-cluster and the local intra-cluster levels.The former leads to misinterpretation of the distances Purse Strap between the various N-D cluster populations, while the latter hampers the appreciation of their individual shapes and composition, which we call cluster appearance.

The distortion of cluster appearance incurred in the 2-D embedding is unavoidable since such low-dimensional embeddings always come at the loss of some of the intra-cluster variance.In this paper, we propose techniques to overcome these limitations by conveying the N-D cluster appearance via a framework inspired by illustrative design.Here we make use of Scagnostics which offers a set of intuitive feature descriptors to describe the appearance of 2-D scatterplots.We extend the Scagnostics analysis to N-D and then devise and test via crowd-sourced user studies a set of parameterizable texture patterns that map to the various Scagnostics descriptors.

Finally, we embed these N-D Scagnostics-informed texture patterns into shapes derived from N-D L-ARGINIME 750 statistics to yield what we call Cluster Appearance Glyphs.We demonstrate our framework with a dataset acquired to analyze program execution times in file systems.