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The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering

Twellmeyer, James and Hutter, Marco and Behrisch, Michael and Kohlhammer, Jörn and Schreck, Tobias (2015):
The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering.
SciTePress, In: IVAPP 2015 : IVAPP 6th International Conference on Information Visualization Theory and Applications, Berlin, Germany, Mar 11, 2015 - Mar 14, 2015, DOI: 10.5220/0005304100400050, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2015
Creators: Twellmeyer, James and Hutter, Marco and Behrisch, Michael and Kohlhammer, Jörn and Schreck, Tobias
Title: The Visual Exploration of Aggregate Similarity for Multi-dimensional Clustering
Language: English
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.

Publisher: SciTePress
Uncontrolled Keywords: Clustering, Information visualization, Visual analytics, Similarity functions, Aggregation functions
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IVAPP 2015 : IVAPP 6th International Conference on Information Visualization Theory and Applications
Event Location: Berlin, Germany
Event Dates: Mar 11, 2015 - Mar 14, 2015
Date Deposited: 08 May 2019 07:10
DOI: 10.5220/0005304100400050
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