Massive Quasi-Clique Detection
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Machine Learning
Cluster graph modification problems
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Graph-Modeled Data Clustering: Exact Algorithms for Clique Generation
Theory of Computing Systems
Invitation to data reduction and problem kernelization
ACM SIGACT News
On the Approximation of Correlation Clustering and Consensus Clustering
Journal of Computer and System Sciences
Aggregating inconsistent information: Ranking and clustering
Journal of the ACM (JACM)
Clustering with Partial Information
MFCS '08 Proceedings of the 33rd international symposium on Mathematical Foundations of Computer Science
Going Weighted: Parameterized Algorithms for Cluster Editing
COCOA 2008 Proceedings of the 2nd international conference on Combinatorial Optimization and Applications
A more effective linear kernelization for cluster editing
Theoretical Computer Science
Fixed-Parameter Algorithms for Cluster Vertex Deletion
Theory of Computing Systems - Special Section: Algorithmic Game Theory; Guest Editors: Burkhard Monien and Ulf-Peter Schroeder
Exact algorithms for cluster editing: evaluation and experiments
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
The cluster editing problem: implementations and experiments
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
Efficient parameterized preprocessing for cluster editing
FCT'07 Proceedings of the 16th international conference on Fundamentals of Computation Theory
New races in parameterized algorithmics
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
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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.