Fixed-parameter tractability of graph modification problems for hereditary properties
Information Processing Letters
Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Local modeling of global interactome networks
Bioinformatics
Clustering with qualitative information
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Going Weighted: Parameterized Algorithms for Cluster Editing
COCOA 2008 Proceedings of the 2nd international conference on Combinatorial Optimization and Applications
A More Relaxed Model for Graph-Based Data Clustering: s-Plex Editing
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Graph-Based Data Clustering with Overlaps
COCOON '09 Proceedings of the 15th Annual International Conference on Computing and Combinatorics
Editing Graphs into Disjoint Unions of Dense Clusters
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Fixed-Parameter Enumerability of Cluster Editing and Related Problems
Theory of Computing Systems
The cluster editing problem: implementations and experiments
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Clustering with local restrictions
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
A More Relaxed Model for Graph-Based Data Clustering: $s$-Plex Cluster Editing
SIAM Journal on Discrete Mathematics
Graph-based data clustering with overlaps
Discrete Optimization
Clustering with local restrictions
Information and Computation
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CLUSTER EDITING is a classical graph theoretic approach to tackle the problem of data set clustering: it consists of modifying a similarity graph into a disjoint union of cliques, i.e, clusters. As pointed out in a number of recent papers, the cluster editing model is too rigid to capture common features of real data sets. Several generalizations have thereby been proposed. In this paper, we introduce (p, q)-cluster graphs, where each cluster misses at most p edges to be a clique, and there are at most q edges between a cluster and other clusters. Our generalization is the first one that allows a large number of false positives and negatives in total, while bounding the number of these locally for each cluster by p and q. We show that recognizing (p, q)-cluster graphs is NP-complete when p and q are input. On the positive side, we show that (0, q)-cluster, (p, 1)-cluster, (p, 2)-cluster, and (1, 3)-cluster graphs can be recognized in polynomial time.