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dc.contributor.authorFuchs, Johannesen_US
dc.contributor.authorIsenberg, Petraen_US
dc.contributor.authorBezerianos, Anastasiaen_US
dc.contributor.authorMiller, Matthiasen_US
dc.contributor.authorKeim, Danielen_US
dc.contributor.editorTarini, Marco and Galin, Ericen_US
dc.date.accessioned2019-05-05T17:46:17Z
dc.date.available2019-05-05T17:46:17Z
dc.date.issued2019
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/eged.20191023
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eged20191023
dc.description.abstractWe present EduClust, a visualization application for teaching clustering algorithms. EduClust is an online application that combines visualizations, interactions, and animations to facilitate the understanding and teaching of clustering steps, parameters, and procedures. Traditional classroom settings aim for cognitive processes like remembering and understanding. We designed EduClust for expanded educational objectives like applying and evaluating. Educators can use the tool in class to show the effect of different clustering parameters on various datasets while animating through each algorithm's steps, but also use the tool to prepare traditional teaching material quickly by exporting animations and images. Students, on the other hand, benefit from the ability to compare and contrast the influence of clustering parameters on different datasets, while seeing technical details such as pseudocode and step-by-step explanations.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectTheory of computation
dc.subjectUnsupervised learning and clustering
dc.subjectApplied computing
dc.subjectInteractive learning environments
dc.titleEduClust - A Visualization Application for Teaching Clustering Algorithmsen_US
dc.description.seriesinformationEurographics 2019 - Education Papers
dc.description.sectionheadersEducate to Visualize
dc.identifier.doi10.2312/eged.20191023
dc.identifier.pages9-16


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