Comparative Study on Optimization Methods for Correlation Clustering

dc.contributor.author	CAPPENBERG, DRIKVY V.
dc.contributor.department	Chalmers tekniska högskola / Institutionen för data och informationsteknik	sv
dc.contributor.examiner	Dubhashi, Devdatt
dc.contributor.supervisor	Haghir Chehreghani, Morteza
dc.date.accessioned	2019-10-08T09:20:03Z
dc.date.available	2019-10-08T09:20:03Z
dc.date.issued	2019	sv
dc.date.submitted	2019
dc.description.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.	sv
dc.identifier.coursecode	DATX05	sv
dc.identifier.uri	https://hdl.handle.net/20.500.12380/300412
dc.language.iso	eng	sv
dc.setspec.uppsok	Technology
dc.subject	Correlation Clustering	sv
dc.subject	Maximization of Agreements	sv
dc.subject	Optimization	sv
dc.subject	Comparative Study	sv
dc.title	Comparative Study on Optimization Methods for Correlation Clustering	sv
dc.type.degree	Examensarbete för masterexamen	sv
dc.type.uppsok	H

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