Monday, 16:15 - 16:40 h, Room: H 3013


Andreas Brieden
Constrained clustering with convex objective function

Coauthor: Peter Gritzmann


In this talk the computation of strongly and weakly balanced clusterings that are optimal with respect to the sum of pairwise cluster distances is considered. As it turns out,
the problem can be reduced to a well studied norm-maximization problem over much lower dimensional polytopes called gravity polytopes. The vertices of these polytopes are closely related to power diagrams which implies nice properties of any optimal clustering and also of approximate solutions. Furthermore this reduction allows for much better approximation results.


Talk 3 of the invited session Mon.3.H 3013
"Constrained clustering" [...]
Cluster 2
"Combinatorial optimization" [...]


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