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Combining linked matrix visualizations with multi-dimensional clustering to detect cybercrime

Poster presented at the International Workshop on Visual Analytics, EuroVA 2012, Vienna, Austria, June 4 - 5, 2012
: Davey, James; Hutter, Marco; May, Thorsten; Kohlhammer, Jörn

Poster urn:nbn:de:0011-n-2293592 (1.5 MByte PDF)
MD5 Fingerprint: 4414e84713f1be34e88d1ddff230b864
Created on: 28.2.2013

Abstract urn:nbn:de:0011-n-229359-19 (885 KByte PDF)
MD5 Fingerprint: 430885d8aeb32266c7052896b642eb81
Created on: 28.2.2013

2012, 1 pp.
International Workshop on Visual Analytics (EuroVA) <3, 2012, Vienna>
Poster, Electronic Publication
Fraunhofer IGD ()
network security; information visualization; visual analytic; cluster analysis; Business Field: Visual decision support; Research Area: Generalized digital documents

Our goal is to find a useful multi-dimensional clustering (MDC) of a data set from the network security domain. Algorithms from the Multi-Criteria Decision Analysis (MCDA) domain are used for the calculation of fused similarity matrices, in which the information from several dimensions are combined. However, the results of MCDA algorithms are often difficult to interpret. We present a linked, interactive, matrix-based visualization which simplifies the comparison of clusters and increases the understanding of MCDA results.