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Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering

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dc.contributor.authorObermeier, Sandra
dc.contributor.authorBeer, Anna
dc.contributor.authorWahl, Florian
dc.contributor.authorSeidl, Thomas
dc.contributor.editorKai-Uwe Sattler
dc.contributor.editorMelanie Herschel
dc.contributor.editorWolfgang Lehner
dc.date.accessioned2021-03-16T07:57:13Z
dc.date.available2021-03-16T07:57:13Z
dc.date.issued2021
dc.description.abstractEven though most clustering algorithms serve knowledge discovery in fields other than computer science, most of them still require users to be familiar with programming or data mining to some extent. As that often prevents efficient research, we developed an easy to use, highly explainable clustering method accompanied by an interactive tool for clustering. It is based on intuitively understandable kNN graphs and the subsequent application of adaptable filters, which can be combined ensemble-like and iteratively and prune unnecessary or misleading edges. For a first overview of the data, fully automatic predefined filter cascades deliver robust results. A selection of simple filters and combination methods that can be chosen interactively yield very good results on benchmark datasets compared to various algorithms.en
dc.identifier.doi10.18420/btw2021-09
dc.identifier.isbn978-3-88579-705-0
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/35814
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-311
dc.subjectClustering
dc.subjectInteractive
dc.subjectkNN
dc.subjectEnsemble
dc.subjectExplainability
dc.titleCluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clusteringen
gi.citation.endPage194
gi.citation.startPage175
gi.conference.date13.-17. September 2021
gi.conference.locationDresden
gi.conference.sessiontitleML & Data Science

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