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Correlation Clustering: maximizing agreements via semidefinite programming
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
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A New Conceptual Clustering Framework
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
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Fitting tree metrics: Hierarchical clustering and Phylogeny
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On the approximability of maximum and minimum edge clique partition problems
CATS '06 Proceedings of the 12th Computing: The Australasian Theroy Symposium - Volume 51
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A divide-and-merge methodology for clustering
ACM Transactions on Database Systems (TODS)
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Efficient location area planning for personal communication systems
IEEE/ACM Transactions on Networking (TON)
The complexity of non-hierarchical clustering with instance and cluster level constraints
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STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
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ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
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ACSC '10 Proceedings of the Thirty-Third Australasian Conferenc on Computer Science - Volume 102
Towards a more discriminative and semantic visual vocabulary
Computer Vision and Image Understanding
The Journal of Machine Learning Research
Clustering causal relationships in genes expression data
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Correlation clustering and consensus clustering
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Approximating the best-fit tree under Lp norms
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WG'05 Proceedings of the 31st international conference on Graph-Theoretic Concepts in Computer Science
Partitioning signed bipartite graphs for classification of individuals and organizations
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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Journal of Computer and System Sciences
On the approximability of maximum and minimum edge clique partition problems
CATS '06 Proceedings of the Twelfth Computing: The Australasian Theory Symposium - Volume 51
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We consider the problem of clustering a collection of elements based on pairwise judgments of similarity and dissimilarity. Bansal, Blum and Chawla [1] cast the problem thus: given a graph G whose edges are labeled "+" (similar) or "-" (dissimilar), partition the vertices into clusters so that the number of pairs correctly (resp. incorrectly) classified with respect to the input labeling is maximized (resp. minimized). Complete graphs, where the classifier labelsevery edge, and general graphs, where some edges are not labeled, are both worth studying. We answer several questions left open in [1] and provide a sound overview of clustering with qualitative information.We give a factor 4 approximation for minimization on complete graphs, and a factor O(log n) approximation for general graphs. For the maximization version, a PTAS for complete graphs is shown in [1]; we give a factor 0.7664 approximation for general graphs, noting that a PTAS is unlikely by proving APX-hardness. We also prove the APX-hardness of minimization on complete graphs.