Please use this identifier to cite or link to this item: https://doi.org/10.1145/2339530.2339774
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dc.titleAssocExplorer: An association rule visualization system for exploratory data analysis
dc.contributor.authorLiu, G.
dc.contributor.authorSuchitra, A.
dc.contributor.authorZhang, H.
dc.contributor.authorFeng, M.
dc.contributor.authorNg, S.-K.
dc.contributor.authorWong, L.
dc.date.accessioned2013-07-04T08:01:23Z
dc.date.available2013-07-04T08:01:23Z
dc.date.issued2012
dc.identifier.citationLiu, G., Suchitra, A., Zhang, H., Feng, M., Ng, S.-K., Wong, L. (2012). AssocExplorer: An association rule visualization system for exploratory data analysis. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : 1536-1539. ScholarBank@NUS Repository. https://doi.org/10.1145/2339530.2339774
dc.identifier.isbn9781450314626
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40310
dc.description.abstractWe present a system called AssocExplorer to support exploratory data analysis via association rule visualization and exploration. AssocExplorer is designed by following the visual information-seeking mantra: overview first, zoom and filter, then details on demand. It effectively uses coloring to deliver information so that users can easily detect things that are interesting to them. If users find a rule interesting, they can explore related rules for further analysis, which allows users to find interesting phenomenon that are difficult to detect when rules are examined separately. Our system also allows users to compare rules and inspect rules with similar item composition but different statistics so that the key factors that contribute to the difference can be isolated. © 2012 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/2339530.2339774
dc.sourceScopus
dc.subjectexploratory data analysis
dc.subjectrule exploration
dc.subjectrule visualization
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/2339530.2339774
dc.description.sourcetitleProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
dc.description.page1536-1539
dc.identifier.isiutNOT_IN_WOS
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