Twellmeyer, James ; Hutter, Marco ; Behrisch, Michael ; Kohlhammer, Jörn ; Schreck, Tobias (2015)
The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering.
IVAPP 2015 : IVAPP 6th International Conference on Information Visualization Theory and Applications. Berlin, Germany (11.03.2015-14.03.2015)
doi: 10.5220/0005304100400050
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We present a visualisation prototype for the support of a novel approach to clustering called TRIAGE. TRIAGE uses aggregation functions which are more adaptable and flexible than the weighted mean for similarity modelling. While TRIAGE has proven itself in practice, the use of complex similarity models makes the interpretation of TRIAGE clusterings challenging. We address this challenge by providing analysts with a linked, matrix-based visualisation of all relevant data attributes. We employ data sampling and matrix seriation to support both effective overviews and fluid, interactive exploration using the same visual metaphor for heterogeneous attributes. The usability of our prototype is demonstrated and assessed with the help of real-world usage scenarios from the cyber-security domain.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Twellmeyer, James ; Hutter, Marco ; Behrisch, Michael ; Kohlhammer, Jörn ; Schreck, Tobias |
| Art des Eintrags: | Bibliographie |
| Titel: | The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering |
| Sprache: | Englisch |
| Publikationsjahr: | März 2015 |
| Verlag: | SciTePress |
| Veranstaltungstitel: | IVAPP 2015 : IVAPP 6th International Conference on Information Visualization Theory and Applications |
| Veranstaltungsort: | Berlin, Germany |
| Veranstaltungsdatum: | 11.03.2015-14.03.2015 |
| DOI: | 10.5220/0005304100400050 |
| Kurzbeschreibung (Abstract): | We present a visualisation prototype for the support of a novel approach to clustering called TRIAGE. TRIAGE uses aggregation functions which are more adaptable and flexible than the weighted mean for similarity modelling. While TRIAGE has proven itself in practice, the use of complex similarity models makes the interpretation of TRIAGE clusterings challenging. We address this challenge by providing analysts with a linked, matrix-based visualisation of all relevant data attributes. We employ data sampling and matrix seriation to support both effective overviews and fluid, interactive exploration using the same visual metaphor for heterogeneous attributes. The usability of our prototype is demonstrated and assessed with the help of real-world usage scenarios from the cyber-security domain. |
| Freie Schlagworte: | Clustering, Information visualization, Visual analytics, Similarity functions, Aggregation functions |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 08 Mai 2019 07:10 |
| Letzte Änderung: | 08 Mai 2019 07:10 |
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