Fixed-Parameter Algorithms for Graph-Modeled Date Clustering

We survey some practical techniques for designing fixed-parameter algorithms for NP-hard graph-modeled data clustering problems. Such clustering problems ask to modify a given graph into a union of dense subgraphs. In particular, we discuss (polynomial-time) kernelizations and depth-bounded search trees and provide concrete applications of these techniques. After that, we shortly review the use of two further algorithmic techniques, iterative compression and average parameterization, applied to graph-modeled data clustering. Finally, we address some challenges for future research.

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