{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T03:16:21Z","timestamp":1777778181871,"version":"3.51.4"},"reference-count":54,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,7,9]],"date-time":"2017-07-09T00:00:00Z","timestamp":1499558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Information Visualization"],"published-print":{"date-parts":[[2019,1]]},"abstract":"<jats:p>Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning\/Data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n\u2009=\u2009160 dimensions.<\/jats:p>","DOI":"10.1177\/1473871617715860","type":"journal-article","created":{"date-parts":[[2017,12,31]],"date-time":"2017-12-31T06:45:23Z","timestamp":1514702723000},"page":"3-32","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Adjustable general line coordinates for visual knowledge discovery in n-D data"],"prefix":"10.1177","volume":"18","author":[{"given":"Boris","family":"Kovalerchuk","sequence":"first","affiliation":[{"name":"Central Washington University, Ellensburg, WA, USA"}]},{"given":"Vladimir","family":"Grishin","sequence":"additional","affiliation":[{"name":"View Trends, Ltd, Port St. Lucie, FL, USA"}]}],"member":"179","published-online":{"date-parts":[[2017,7,9]]},"reference":[{"key":"bibr1-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2011.229"},{"key":"bibr2-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1201\/b10683"},{"key":"bibr3-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68628-8"},{"key":"bibr4-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71080-6"},{"key":"bibr5-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1007\/b138081"},{"key":"bibr6-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1057\/palgrave.ivs.9500003"},{"key":"bibr7-1473871617715860","first-page":"3","volume-title":"Scientific visualization\u2014overviews, methodologies and techniques","author":"Wong P","year":"1997"},{"key":"bibr8-1473871617715860","first-page":"437","volume-title":"Proceedings of the visualization\u201997","author":"Hoffman P"},{"key":"bibr9-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1186\/s40679-015-0006-6"},{"key":"bibr10-1473871617715860","first-page":"22","volume-title":"Proceedings of the IEEE information visualization symposium","volume":"650","author":"Kandogan E"},{"key":"bibr11-1473871617715860","first-page":"107","volume-title":"Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining","author":"Kandogan E"},{"key":"bibr12-1473871617715860","volume-title":"Proceedings of the IS&T\/SPIE electronic imaging: visualization and data analysis","author":"Kovalerchuk B"},{"key":"bibr13-1473871617715860","first-page":"27","volume-title":"Proceedings of the international conference on cooperative design, visualization and engineering","author":"Grishin V"},{"key":"bibr14-1473871617715860","first-page":"19","volume-title":"Proceedings of the international conference on cooperative design, visualization and engineering","author":"Kovalerchuk B"},{"key":"bibr15-1473871617715860","first-page":"1","volume-title":"Proceedings of the 2015 annual conference of the North American fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC)","author":"Kovalerchuk B"},{"key":"bibr16-1473871617715860","first-page":"1803","volume-title":"Proceedings of the 2016 international joint conference on world computational intelligence congress, neural networks (IJCNN)","author":"Kovalerchuk B"},{"key":"bibr17-1473871617715860","first-page":"105","volume-title":"Proceedings of the 9th annual IEEE conference on information visualization (INFOVIS 2003)","author":"Yang J"},{"key":"bibr18-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2015.2466992"},{"key":"bibr19-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-1904-8"},{"key":"bibr20-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2015.2467553"},{"key":"bibr21-1473871617715860","doi-asserted-by":"publisher","DOI":"10.4135\/9781412985130"},{"key":"bibr22-1473871617715860","first-page":"27","volume":"41","author":"Mead A","year":"1992","journal-title":"J Roy Stat Soc D: Sta"},{"key":"bibr23-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2008.173"},{"key":"bibr24-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1177\/1473871612439357"},{"key":"bibr25-1473871617715860","volume-title":"Self-organization and associative memory","author":"Kohonen T","year":"1984"},{"key":"bibr26-1473871617715860","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73750-6"},{"key":"bibr27-1473871617715860","unstructured":"Duch W, Adamczak R, Gr\u0105bczewski K, Extraction of knowledge from data using computational intelligence methods. 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