Speaker
Prof.
Klemenes Böhm
(KIT)
Description
Outlier mining and clustering are important when analyzing big data.
Outliers are objects that deviate from regular objects in their
neighborhood by much, clusters in turn are sets of objects with very
little deviation from each other. In many applications, outliers and
clusters do not show up in the full space, only in subspaces.
Identifying subspaces likely to contain outliers is the open research
issue our research is currently addressing. In this presentation, we
present three subspace-search methods we have proposed recently. We
also explain their importance for various scientific domains present
at KIT.
Primary author
Prof.
Klemenes Böhm
(KIT)
