Abstract
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 \(\mathcal{NP}\)-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.
This work was partially supported by the DFG under grant WA 654/19-1.
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Görke, R., Schumm, A., Wagner, D. (2011). Density-Constrained Graph Clustering. In: Dehne, F., Iacono, J., Sack, JR. (eds) Algorithms and Data Structures. WADS 2011. Lecture Notes in Computer Science, vol 6844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22300-6_58
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DOI: https://doi.org/10.1007/978-3-642-22300-6_58
Publisher Name: Springer, Berlin, Heidelberg
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