Campus Event Calendar
Event Entry
New for: D2, D3
What and Who
Extensions to Transitivity Clustering
Richard Röttger
International Max Planck Research School for Computer Science - IMPRS
IMPRS Research Seminar
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Monday, 11 July 2011
12:00
60 Minutes
E1 4
024
Saarbrücken
Abstract
Clustering objects into classes such that elements within one group
share common traits is a long-standing challenge, not just in
computational biology. However, in bioinformatics clustering
algorithms play an important role for typical data analysis tasks, for
instance, remote protein homology detection. In the talk, I will
introduce Transitivity Clustering, an approach based on the NP-hard
weighted transitive graph projection problem. It ensures that, given a
pairwise similarity function and a threshold, the average similarity
of objects from the same cluster is above the threshold and the
average similarity between objects from different clusters is below
the threshold. We tackle this hard problem with heuristics and exact
fixed parameter approaches.
share common traits is a long-standing challenge, not just in
computational biology. However, in bioinformatics clustering
algorithms play an important role for typical data analysis tasks, for
instance, remote protein homology detection. In the talk, I will
introduce Transitivity Clustering, an approach based on the NP-hard
weighted transitive graph projection problem. It ensures that, given a
pairwise similarity function and a threshold, the average similarity
of objects from the same cluster is above the threshold and the
average similarity between objects from different clusters is below
the threshold. We tackle this hard problem with heuristics and exact
fixed parameter approaches.
Contact
Jennifer Gerling
1800
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Jennifer Gerling, 07/07/2011 15:28 -- Created document.