Graph-theoretic scagnostics

By Leland Wilkinson, Anushka Anand, Robert L. Grossman. IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., 2005

342 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8bc9868fe6c936614f7f94b01757723e9ffaaa43

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In Artificial Intelligence

In Computational Theory and Mathematics

In Computer Vision and Pattern Recognition

Citations in the corpus

Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, neutral): “Scagnostics [131] are measures that characterize the point distribution in 2D scatterplots and can be used to detect anomalies in shape, density, and trend.”
Statistical inference for exploratory data analysis and model diagnostics (2009, neutral): “Such an approach has indeed been attempted for scatterplots by Tukey & Tukey (1985) who coined the term ‘scagnostics’; more recently Wilkinson et al. (2005) revived the idea with a list of features that includes ‘outlying’, ‘skewed’, ‘clumpy’, ‘sparse’, ‘striated’, ‘convex’, ‘skinny’, ‘stringy’ and…”
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization (2011, methodology): “An example is Graph-Theoretic Scagnostics [54] a technique where it is possible to characterize scatter plots with features like “stringy” or “skinny”.”
A Layered Grammar of Graphics (2010, background): “The most important modern work in graphical grammars is “The Grammar of Graph-ics” by Wilkinson, Anand, and Grossman (2005).”
Voyager 2: Augmenting Visual Analysis with Partial View Specifications (2017, background): “Quality metrics [4] and scagnostics [1, 36] can also help identify interesting data ﬁelds.”
Visualizing High-Dimensional Data: Advances in the Past Decade (2017, positive): “They both provide meaningful abstractions of the high-dimensional data, which reduces the amount of data needed to be processed or stored; and they utilize sophisticated hierarchical representations capturing features at multiple scales, which enables progressive simpliﬁcations of features…”
Handbook of Big Data (2016, background): “There has been interesting research on cognostics for scatterplots, called scagnostics, that has yielded metrics that quantify different shapes or behaviors that might present themselves in a scatterplot [21,22].”