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The partial weighted set cover problem with applications to outlier detection and clustering

 
: Bothe, S.; Horvath, T.

:
Volltext ()

Krestel, R.:
LWDA 2016, Lernen, Wissen, Daten, Analysen : Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" Potsdam, Germany, September 12-14, 2016
Potsdam, 2016 (CEUR Workshop Proceedings 1670)
http://ceur-ws.org/Vol-1670/
ISSN: 1613-0073
S.335-346
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2016, Potsdam>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
We define the partial weighted set cover problem, a generic combinatorial optimization problem, that includes some classical data mining problems as special cases. We prove that it is computationally intractable and give a local search algorithm for this problem. As application examples, we then show how to translate clustering and outlier detection problems into this generic problem. Our experiments on synthetic and real-world datasets indicate that the quality of the solution produced by the generic local search algorithm is comparable to that obtained by state-of-The-Art clustering and outlier detection algorithms.

: http://publica.fraunhofer.de/dokumente/N-422723.html