The solution of some random NP-hard problems in polynomial expected time
Journal of Algorithms
Hill-climbing finds random planted bisections
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Heuristics for semirandom graph problems
Journal of Computer and System Sciences
Algorithms for Graph Partitioning on the Planted Partition Model
RANDOM-APPROX '99 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial Algorithms and Techniques
Eigenvalues and graph bisection: An average-case analysis
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Simulated annealing for graph bisection
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
Machine Learning
Max Cut for Random Graphs with a Planted Partition
Combinatorics, Probability and Computing
Clustering Algorithms for Chains
The Journal of Machine Learning Research
Boosting spectral partitioning by sampling and iteration
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Bounding the misclassification error in spectral partitioning in the planted partition model
WG'05 Proceedings of the 31st international conference on Graph-Theoretic Concepts in Computer Science
Reconstructing many partitions using spectral techniques
FCT'05 Proceedings of the 15th international conference on Fundamentals of Computation Theory
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The following probabilistic process models the generation of noisy clustering data: Clusters correspond to disjoint sets of vertices in a graph. Each two vertices from the same set are connected by an edge with probability p, and each two vertices from different sets are connected by an edge with probability r p. The goal of the clustering problem is to reconstruct the clusters from the graph. We give algorithms that solve this problem with high probability. Compared to previous studies, our algorithms have lower time complexity and wider parameter range of applicability. In particular, our algorithms can handle O(驴n/ log n) clusters in an n-vertex graph, while all previous algorithms require that the number of clusters is constant.