Algorithms for clustering data
Algorithms for clustering data
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Engineering graph clustering: Models and experimental evaluation
Journal of Experimental Algorithmics (JEA)
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Clusterings of graphs are often constructed and evaluated with the aid of a quality measure. Numerous such measures exist, some of which adapt an established measure for graph cuts to clusterings. In this work we pursue the problem of finding clusterings which simultaneously feature guaranteed intra- and good intercluster quality. To this end we systematically assemble a range of cut-based bicriteria measures and, after showing NP-hardness for some, focus on the classic heuristic of constrained greedy agglomeration. We identify key behavioral traits of a measure, (dis-)prove them for each one proposed and show how these translate to algorithmic efficiency.