Chalmers tekniska högskola / Institutionen för data och informationsteknik
Abstract
Correlation clustering is an optimization problem that aims to create partition of
data based on pairwise similarity coefficients that represents the level of similarities
between data observations. The thesis focused on the maximization of agreements
version of the problem in which to find clustering of data where the data that belong
to the same cluster have maximized agreements. The thesis aims to give more
details on how different methods are used for correlation clustering problem, how
they perform and what are the similarities between these methods. The well known
linear programming methods as well as simple iterative algorithms are compared in
terms of their runtime and correctness
