Friday, 15:45 - 16:10 h, Room: H 2053

 

Daniel Aloise
A column generation algorithm for semi-supervised clustering

Coauthors: Pierre Hansen, Caroline Rocha

 

Abstract:
Clustering is a powerful tool for automated analysis of data. It addresses the following problem: given a set of entities find subsets, called clusters, which are homogeneous and/or well separated. In addition to the entities themselves, in many applications, information is also available regarding their relations in the space. This work presents
a column generation algorithm for minimum sum-of-squares clustering in the presence of must-link and cannot-link pairwise constraints. The computational results show that the proposed algorithm is faster
than the current state-of-the-art method.

 

Talk 2 of the contributed session Fri.3.H 2053
"Advances in global optimization VI" [...]
Cluster 9
"Global optimization" [...]

 

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