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Visual Growing Neural Gas for Exploratory Data Analysis
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab)
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab)ORCID iD: 0000-0003-2900-9335
2019 (English)In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: Volume 3: IVAPP, 58-71, 2019, Prague, Czech Republic / [ed] Andreas Kerren, Christophe Hurter, Jose Braz, SciTePress, 2019, Vol. 3, p. 58-71Conference paper, Published paper (Refereed)
Abstract [en]

This paper argues for the use of a topology learning algorithm, the Growing Neural Gas (GNG), for providing an overview of the structure of large and multidimensional datasets that can be used in exploratory data analysis. We introduce a generic, off-the-shelf library, Visual GNG, developed using the Big Data framework Apache Spark, which provides an incremental visualization of the GNG training process, and enables user-in-the-loop interactions where users can pause, resume or steer the computation by changing optimization parameters. Nine case studies were conducted with domain experts from different areas, each working on unique real-world datasets. The results show that Visual GNG contributes to understanding the distribution of multidimensional data; finding which features are relevant in such distribution; estimating the number of k clusters to be used in traditional clustering algorithms, such as K-means; and finding outliers.

Place, publisher, year, edition, pages
SciTePress, 2019. Vol. 3, p. 58-71
Keywords [en]
Growing Neural Gas, Dimensionality Reduction, Multidimensional Data, Visual Analytics, Exploratory Data Analysis
National Category
Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16756DOI: 10.5220/0007364000580071Scopus ID: 2-s2.0-85064748097ISBN: 978-989-758-354-4 (print)OAI: oai:DiVA.org:his-16756DiVA, id: diva2:1303143
Conference
14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 25-27, 2019, Prague, Czech Republic
Available from: 2019-04-08 Created: 2019-04-08 Last updated: 2019-09-30Bibliographically approved

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Ventocilla, ElioRiveiro, Maria

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  • apa
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