Towards Visualizing Hidden Structures

Rémy Dautriche 1, 2 Alexandre Termier 3, 4 Renaud Blanch 1 Miguel Santana 2
1 LIG Laboratoire d'Informatique de Grenoble - IIHM
LIG - Laboratoire d'Informatique de Grenoble, Inria - Institut National de Recherche en Informatique et en Automatique
3 LACODAM - Large Scale Collaborative Data Mining
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE, Inria Rennes – Bretagne Atlantique
Abstract : There is an increasing need to quickly understand the contents log data. A wide range of patterns can be computed and provide valuable information: for example existence of repeated sequences of events or periodic behaviors. However pattern mining techniques often produce many patterns that have to be examined one by one, which is time consuming for experts. On the other hand, visualization techniques are easier to understand , but cannot provide the in-depth understanding provided by pattern mining approaches. Our contribution is to propose a novel visual analytics method that allows to immediately visualize hidden structures such as repeated sets/sequences and periodicity, allowing to quickly gain a deep understanding of the log.
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Rémy Dautriche, Alexandre Termier, Renaud Blanch, Miguel Santana. Towards Visualizing Hidden Structures. International Conference on Data Mining (ICDM) / PhD Forum, 2016, Barcelone, Spain. ⟨hal-01407664⟩

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