Panning for Insight: Amplifying Insight through Tight Integration of Machine Learning, Data Mining, and Visualization

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dc.contributor.authorKarer, Benjaminen_US
dc.contributor.authorScheler, Ingaen_US
dc.contributor.authorHagen, Hansen_US
dc.contributor.editorIan Nabney and Jaakko Peltonen and Daniel Archambaulten_US
dc.date.accessioned2018-06-02T17:50:59Z
dc.date.available2018-06-02T17:50:59Z
dc.date.issued2018
dc.description.abstractWith the rapid progress made in Data Mining, Visualization, and Machine Learning during the last years, combinations of these methods have gained increasing interest. This paper summarizes ideas behind ongoing work on combining methods of these three domains into an insight-driven interactive data analysis workflow. Based on their interpretation of data visualizations, users generate metadata to be fed back into the analysis. The resulting resonance effect improves the performance of subsequent analysis. The paper outlines the ideas behind the workflow, indicates the benefits and discusses how to avoid potential pitfalls.en_US
dc.description.sectionheadersPaper
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20181130
dc.identifier.isbn978-3-03868-062-8
dc.identifier.pages1-5
dc.identifier.urihttps://doi.org/10.2312/mlvis.20181130
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mlvis20181130
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectVisualization theory
dc.subjectconcepts and paradigms
dc.titlePanning for Insight: Amplifying Insight through Tight Integration of Machine Learning, Data Mining, and Visualizationen_US
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Machine Learning Methods in Visualisation for Big Data 2018