{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:25Z","timestamp":1740122665572,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007511","name":"Universidad Rey Juan Carlos","doi-asserted-by":"publisher","award":["C1PREDOC2020","C1PREDOC2020"],"award-info":[{"award-number":["C1PREDOC2020","C1PREDOC2020"]}],"id":[{"id":"10.13039\/501100007511","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2021-122640OB-100","PID2021-122640OB-100","PID2021-122640OB-100"],"award-info":[{"award-number":["PID2021-122640OB-100","PID2021-122640OB-100","PID2021-122640OB-100"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s10489-023-05149-4","type":"journal-article","created":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T07:02:16Z","timestamp":1703574136000},"page":"924-946","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CSViz: Class Separability Visualization for high-dimensional datasets"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1880-9225","authenticated-orcid":false,"given":"Marina","family":"Cuesta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4674-1598","authenticated-orcid":false,"given":"Carmen","family":"Lancho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0848-1190","authenticated-orcid":false,"given":"Alberto","family":"Fern\u00e1ndez-Isabel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6101-9755","authenticated-orcid":false,"given":"Emilio L.","family":"Cano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5197-2932","authenticated-orcid":false,"given":"Isaac","family":"Mart\u00edn\u00a0De\u00a0Diego","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"issue":"1","key":"5149_CR1","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1109\/MCG.2021.3130314","volume":"42","author":"N Andrienko","year":"2022","unstructured":"Andrienko N, Andrienko G, Adilova L, Wrobel S (2022) Visual analytics for human-centered machine learning. IEEE Comput Graph Appl 42(1):123\u2013133","journal-title":"IEEE Comput Graph Appl"},{"key":"5149_CR2","doi-asserted-by":"crossref","unstructured":"Aupetit M, Sedlmair M (2016) Sepme: 2002 new visual separation measures. In: 2016 IEEE pacific visualization symposium (PacificVis), IEEE, pp 1\u20138","DOI":"10.1109\/PACIFICVIS.2016.7465244"},{"key":"5149_CR3","doi-asserted-by":"crossref","unstructured":"Aupetit M, Ali A, Baggag A, Bensmail H (2022) Classmat: a matrix of small multiples to analyze the topology of multiclass multidimensional data. In: 2022 Topological data analysis and visualization (TopoInVis), IEEE, pp 70\u201380","DOI":"10.1109\/TopoInVis57755.2022.00014"},{"key":"5149_CR4","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.inffus.2020.01.005","volume":"59","author":"S Ayesha","year":"2020","unstructured":"Ayesha S, Hanif MK, Talib R (2020) Overview and comparative study of dimensionality reduction techniques for high dimensional data. Info Fusion 59:44\u201358","journal-title":"Info Fusion"},{"key":"5149_CR5","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.cag.2021.03.004","volume":"96","author":"J Bernard","year":"2021","unstructured":"Bernard J, Hutter M, Zeppelzauer M, Sedlmair M, Munzner T (2021) Proseco: Visual analysis of class separation measures and dataset characteristics. Comput & Graph 96:48\u201360","journal-title":"Comput & Graph"},{"issue":"9","key":"5149_CR6","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern recognition 37(9):1757\u20131771","journal-title":"Pattern recognition"},{"key":"5149_CR7","doi-asserted-by":"publisher","DOI":"10.1201\/9781351072304","volume-title":"Graphical methods for data analysis","author":"JM Chambers","year":"2018","unstructured":"Chambers JM (2018) Graphical methods for data analysis. CRC Press"},{"key":"5149_CR8","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1111\/cgf.13404","volume":"37","author":"M Chegini","year":"2018","unstructured":"Chegini M, Shao L, Gregor R, Lehmann DJ, Andrews K, Schreck T (2018) Interactive visual exploration of local patterns in large scatterplot spaces. Computer graphics forum, wiley online library 37:99\u2013109","journal-title":"Computer graphics forum, wiley online library"},{"key":"5149_CR9","doi-asserted-by":"crossref","unstructured":"Cleveland WS, Grosse E, Shyu WM (2017) Local regression models. In: Statistical models in S, Routledge, pp 309\u2013376","DOI":"10.1201\/9780203738535-8"},{"key":"5149_CR10","doi-asserted-by":"publisher","first-page":"81555","DOI":"10.1109\/ACCESS.2019.2923736","volume":"7","author":"W Cui","year":"2019","unstructured":"Cui W (2019) Visual analytics: A comprehensive overview. IEEE Access 7:81555\u201381573","journal-title":"IEEE Access"},{"key":"5149_CR11","unstructured":"Cui W, Strazdins G, Wang H (2021) Visual analysis of multidimensional big data: A scalable lightweight bundling method for parallel coordinates. IEEE Trans Big Data"},{"issue":"3","key":"5149_CR12","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1080\/00273171.2020.1743631","volume":"56","author":"M Del Giudice","year":"2021","unstructured":"Del Giudice M (2021) Effective dimensionality: A tutorial. Multivar Behav Res 56(3):527\u2013542","journal-title":"Multivar Behav Res"},{"key":"5149_CR13","unstructured":"Dua D, Graff C (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"issue":"536","key":"5149_CR14","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1080\/01621459.2021.1938081","volume":"116","author":"A Gelman","year":"2021","unstructured":"Gelman A, Vehtari A (2021) What are the most important statistical ideas of the past 50 years. J Am Stat Assoc 116(536):2087\u20132097","journal-title":"J Am Stat Assoc"},{"key":"5149_CR15","doi-asserted-by":"crossref","unstructured":"Goh WWB, Foo RJK, Wong L (2022) What can scatterplots teach us about doing data science better. Int J Data Sci Anal pp 1\u201315","DOI":"10.21203\/rs.3.rs-1733113\/v1"},{"issue":"1","key":"5149_CR16","doi-asserted-by":"publisher","first-page":"82","DOI":"10.3390\/e22010082","volume":"22","author":"AN Gorban","year":"2020","unstructured":"Gorban AN, Makarov VA, Tyukin IY (2020) High-dimensional brain in a high-dimensional world: Blessing of dimensionality. Entropy 22(1):82","journal-title":"Entropy"},{"key":"5149_CR17","first-page":"507","volume":"35","author":"L Grinsztajn","year":"2022","unstructured":"Grinsztajn L, Oyallon E, Varoquaux G (2022) Why do tree-based models still outperform deep learning on typical tabular data. Adv Neural Inf Process Syst 35:507\u2013520","journal-title":"Adv Neural Inf Process Syst"},{"key":"5149_CR18","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s41060-020-00240-2","volume":"11","author":"V Grossi","year":"2021","unstructured":"Grossi V, Giannotti F, Pedreschi D, Manghi P, Pagano P, Assante M (2021) Data science: a game changer for science and innovation. Int J Data Sci Anal 11:263\u2013278","journal-title":"Int J Data Sci Anal"},{"key":"5149_CR19","doi-asserted-by":"crossref","unstructured":"Guyon I, Sun-Hosoya L, Boull\u00e9 M, Escalante HJ, Escalera S, Liu Z, Jajetic D, Ray B, Saeed M, Sebag M, et\u00a0al (2019) Analysis of the automl challenge series. Autom Mach Learn 177","DOI":"10.1007\/978-3-030-05318-5_10"},{"issue":"3","key":"5149_CR20","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/34.990132","volume":"24","author":"TK Ho","year":"2002","unstructured":"Ho TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289\u2013300","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5149_CR21","doi-asserted-by":"crossref","unstructured":"Jo J, Seo J (2019) Disentangled representation of data distributions in scatterplots. In: 2019 IEEE Visualization conference (VIS), IEEE, pp 136\u2013140","DOI":"10.1109\/VISUAL.2019.8933670"},{"key":"5149_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115686","volume":"186","author":"A Kaur","year":"2021","unstructured":"Kaur A, Chauhan APS, Aggarwal AK (2021) An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network. Expert Systems with Applications 186:115686","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"5149_CR23","doi-asserted-by":"publisher","first-page":"144","DOI":"10.4097\/kjae.2017.70.2.144","volume":"70","author":"SG Kwak","year":"2017","unstructured":"Kwak SG, Kim JH (2017) Central limit theorem: the cornerstone of modern statistics. Korean J Anesthesiol 70(2):144\u2013156","journal-title":"Korean J Anesthesiol"},{"issue":"7","key":"5149_CR24","doi-asserted-by":"publisher","first-page":"8073","DOI":"10.1007\/s10489-022-03793-w","volume":"53","author":"C Lancho","year":"2023","unstructured":"Lancho C, Mart\u00edn De Diego I, Cuesta M, Acena V, Moguerza JM (2023) Hostility measure for multi-level study of data complexity. Appl Intell 53(7):8073\u20138096","journal-title":"Appl Intell"},{"key":"5149_CR25","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1111\/cgf.12639","volume":"34","author":"S Liu","year":"2015","unstructured":"Liu S, Wang B, Thiagarajan JJ, Bremer PT, Pascucci V (2015) Visual exploration of high-dimensional data through subspace analysis and dynamic projections. Computer graphics forum, Wiley Online Library 34:271\u2013280","journal-title":"Computer graphics forum, Wiley Online Library"},{"key":"5149_CR26","doi-asserted-by":"crossref","unstructured":"Lorena AC, Garcia LP, Lehmann J, Souto MC, Ho TK (2019) How complex is your classification problem? a survey on measuring classification complexity. ACM Comput Surv (CSUR) 52(5):1\u201334","DOI":"10.1145\/3347711"},{"key":"5149_CR27","doi-asserted-by":"crossref","unstructured":"Ma Y, Tung AK, Wang W, Gao X, Pan Z, Chen W (2018) Scatternet: A deep subjective similarity model for visual analysis of scatterplots. IEEE Trans Vis Comput Graph 26(3):1562\u20131576","DOI":"10.1109\/TVCG.2018.2875702"},{"issue":"9","key":"5149_CR28","volume":"1","author":"SR Midway","year":"2020","unstructured":"Midway SR (2020) Principles of effective data visualization. Patterns 1(9):100141","journal-title":"Principles of effective data visualization. Patterns"},{"key":"5149_CR29","doi-asserted-by":"crossref","unstructured":"Nguyen QV, Miller N, Arness D, Huang W, Huang ML, Simoff S (2020) Evaluation on interactive visualization data with scatterplots. Vis Inform 4(4):1\u201310","DOI":"10.1016\/j.visinf.2020.09.004"},{"issue":"1","key":"5149_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-0416-x","volume":"12","author":"D Probst","year":"2020","unstructured":"Probst D, Reymond JL (2020) Visualization of very large high-dimensional data sets as minimum spanning trees. Journal of Cheminformatics 12(1):1\u201313","journal-title":"Journal of Cheminformatics"},{"key":"5149_CR31","unstructured":"R Core Team (2022) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https:\/\/www.R-project.org\/"},{"key":"5149_CR32","unstructured":"Schloerke B, Cook D, Larmarange J, Briatte F, Marbach M, Thoen E, Elberg A, Crowley J (2021) GGally: Extension to \u2019ggplot2\u2019. https:\/\/CRAN.R-project.org\/package=GGally, r package version 2.1.2"},{"key":"5149_CR33","doi-asserted-by":"crossref","unstructured":"Shao L, Silva N, Eggeling E, Schreck T (2017) Visual exploration of large scatter plot matrices by pattern recommendation based on eye tracking. In: Proceedings of the 2017 ACM workshop on exploratory search and interactive data analytics, pp 9\u201316","DOI":"10.1145\/3038462.3038463"},{"key":"5149_CR34","unstructured":"Sigillito VG, Wing SP, Hutton LV, Baker KB (1989) Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest 10(3):262\u2013266"},{"key":"5149_CR35","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s10994-013-5422-z","volume":"95","author":"MR Smith","year":"2014","unstructured":"Smith MR, Martinez T, Giraud-Carrier C (2014) An instance level analysis of data complexity. Mach Learn 95:225\u2013256","journal-title":"Mach Learn"},{"key":"5149_CR36","doi-asserted-by":"crossref","unstructured":"Triguero I, Gonz\u00e1lez S, Moyano JM, Garc\u00eda S, Alcal\u00e1-Fdez J, Luengo J, Fern\u00e1ndez A, del Jes\u00fas MJ, S\u00e1nchez L, Herrera F (2017) Keel 3.0: an open source software for multi-stage analysis in data mining. Int J Comput Intell Syst 10(1):1238\u20131249","DOI":"10.2991\/ijcis.10.1.82"},{"issue":"2","key":"5149_CR37","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2014","unstructured":"Vanschoren J, Van Rijn JN, Bischl B, Torgo L (2014) Openml: networked science in machine learning. ACM SIGKDD Explor Newsl 15(2):49\u201360","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"5149_CR38","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.future.2018.08.007","volume":"91","author":"S Wan","year":"2019","unstructured":"Wan S, Zhao Y, Wang T, Gu Z, Abbasi QH, Choo KKR (2019) Multi-dimensional data indexing and range query processing via voronoi diagram for internet of things. Futur Gener Comput Syst 91:382\u2013391","journal-title":"Futur Gener Comput Syst"},{"issue":"1","key":"5149_CR39","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1177\/1473871617733996","volume":"18","author":"J Wang","year":"2019","unstructured":"Wang J, Liu X, Shen HW (2019) High-dimensional data analysis with subspace comparison using matrix visualization. Inf Vis 18(1):94\u2013109","journal-title":"Inf Vis"},{"issue":"12","key":"5149_CR40","doi-asserted-by":"publisher","first-page":"5134","DOI":"10.1109\/TVCG.2021.3106142","volume":"28","author":"Q Wang","year":"2021","unstructured":"Wang Q, Chen Z, Wang Y, Qu H (2021) A survey on ml4vis: Applying machine learning advances to data visualization. IEEE Trans Vis Comput Graph 28(12):5134\u20135153","journal-title":"IEEE Trans Vis Comput Graph"},{"issue":"1","key":"5149_CR41","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1109\/TVCG.2019.2934796","volume":"26","author":"Y Wang","year":"2019","unstructured":"Wang Y, Wang Z, Liu T, Correll M, Cheng Z, Deussen O, Sedlmair M (2019) Improving the robustness of scagnostics. IEEE Trans Vis Comput Graph 26(1):759\u2013769","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"5149_CR42","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s41095-020-0191-7","volume":"7","author":"J Yuan","year":"2021","unstructured":"Yuan J, Chen C, Yang W, Liu M, Xia J, Liu S (2021) A survey of visual analytics techniques for machine learning. Computational Visual Media 7:3\u201336","journal-title":"Computational Visual Media"},{"issue":"12","key":"5149_CR43","doi-asserted-by":"publisher","first-page":"2625","DOI":"10.1109\/TVCG.2013.150","volume":"19","author":"X Yuan","year":"2013","unstructured":"Yuan X, Ren D, Wang Z, Guo C (2013) Dimension projection matrix\/tree: Interactive subspace visual exploration and analysis of high dimensional data. IEEE Trans Vis Comput Graph 19(12):2625\u20132633","journal-title":"IEEE Trans Vis Comput Graph"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05149-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05149-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05149-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T10:26:08Z","timestamp":1705141568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05149-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["5149"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05149-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,12,26]]},"assertion":[{"value":"1 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing of interest"}},{"value":"Not applicable since all datasets analysed during the current study are available in the well-known public repositories <i>KEEL<\/i> [] (), <i>UCI Machine Learning<\/i> [] () and <i>OpenML<\/i> [] ().","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}