{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:40:06Z","timestamp":1760236806392,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1527200","IIS-1117132"],"award-info":[{"award-number":["IIS-1527200","IIS-1117132"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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 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 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.<\/jats:p>","DOI":"10.3390\/info13010003","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T10:16:18Z","timestamp":1640254578000},"page":"3","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cluster Appearance Glyphs: A Methodology for Illustrating High-Dimensional Data Patterns in 2-D Data Layouts"],"prefix":"10.3390","volume":"13","author":[{"given":"Jenny Hyunjung","family":"Lee","sequence":"first","affiliation":[{"name":"Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darius","family":"Coelho","sequence":"additional","affiliation":[{"name":"Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0996-8590","authenticated-orcid":false,"given":"Klaus","family":"Mueller","sequence":"additional","affiliation":[{"name":"Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11794, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"unstructured":"Hey, T., Tansley, S., and Tolle, K. 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