Bernard, Jürgen ; Dobermann, Eduard ; Sedlmair, Michael ; Fellner, Dieter W. (2017)
Combining Cluster and Outlier Analysis with Visual Analytics.
8. International EuroVis Workshop on Visual Analytics (EuroVA).
doi: 10.2312/eurova.20171114
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In this work, we present a visual analytics tool that allows the combined analysis of clusters and outliers. Users can add multiple clustering and outlier analysis algorithms, compare results visually, and combine the algorithms' results. The usefulness of the combined analysis is demonstrated using the example of labeling unknown data sets. The usage scenario also shows that identified clusters and outliers can share joint areas of the data space.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Bernard, Jürgen ; Dobermann, Eduard ; Sedlmair, Michael ; Fellner, Dieter W. |
| Art des Eintrags: | Bibliographie |
| Titel: | Combining Cluster and Outlier Analysis with Visual Analytics |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Ort: | Goslar |
| Buchtitel: | EuroVA 2017 |
| Veranstaltungstitel: | 8. International EuroVis Workshop on Visual Analytics (EuroVA) |
| DOI: | 10.2312/eurova.20171114 |
| Kurzbeschreibung (Abstract): | Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In this work, we present a visual analytics tool that allows the combined analysis of clusters and outliers. Users can add multiple clustering and outlier analysis algorithms, compare results visually, and combine the algorithms' results. The usefulness of the combined analysis is demonstrated using the example of labeling unknown data sets. The usage scenario also shows that identified clusters and outliers can share joint areas of the data space. |
| Freie Schlagworte: | Information systems, Data mining, Human-centered computing, Visual analytics, Information visualization, Machine learning, Active learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 04 Mai 2020 08:54 |
| Letzte Änderung: | 04 Feb 2022 12:39 |
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