DaaG: Visual Analytics Clustering Using Network Representation

dc.contributor.author	Alcaide, Daniel	en_US
dc.contributor.author	Aerts, Jan	en_US
dc.contributor.editor	Anna Puig Puig and Tobias Isenberg	en_US
dc.date.accessioned	2017-06-12T05:17:57Z
dc.date.available	2017-06-12T05:17:57Z
dc.date.issued	2017
dc.description.abstract	Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common solutions is the identification and representation of clusters. In this work, we propose a visual analytics clustering methodology for guiding the user in the exploration and detection of clusters in a dataset. We thereby combine the homological algebra with a graphical representation of the clustered dataset as a network into one coherent framework. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis.	en_US
dc.description.sectionheaders	Posters
dc.description.seriesinformation	EuroVis 2017 - Posters
dc.identifier.doi	10.2312/eurp.20171169
dc.identifier.isbn	978-3-03868-044-4
dc.identifier.pages	61-63
dc.identifier.uri	https://doi.org/10.2312/eurp.20171169
dc.identifier.uri	https://diglib.eg.org:443/handle/10.2312/eurp20171169
dc.publisher	The Eurographics Association	en_US
dc.subject	H.3.3 [Information Search and Retrieval]
dc.subject	Clustering
dc.title	DaaG: Visual Analytics Clustering Using Network Representation	en_US

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