On clusterings: Good, bad and spectral
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[1] David R. Karger,et al. Approximating s – t Minimum Cuts in ~ O(n 2 ) Time , 2007 .
[2] Anna R. Karlin,et al. Spectral analysis of data , 2001, STOC '01.
[3] Charles J. Alpert,et al. Spectral Partitioning: The More Eigenvectors, The Better , 1995, 32nd Design Automation Conference.
[4] Mark Jerrum,et al. Approximate Counting, Uniform Generation and Rapidly Mixing Markov Chains , 1987, International Workshop on Graph-Theoretic Concepts in Computer Science.
[5] Jitendra Malik,et al. Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[6] Frank Thomson Leighton,et al. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms , 1999, JACM.
[7] Shang-Hua Teng,et al. Spectral partitioning works: planar graphs and finite element meshes , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[8] Yair Weiss,et al. Segmentation using eigenvectors: a unifying view , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[9] David B. Shmoys,et al. A constant-factor approximation for the k-median problem , 1999 .
[10] Rajeev Motwani,et al. Incremental Clustering and Dynamic Information Retrieval , 2004, SIAM J. Comput..
[11] Santosh S. Vempala,et al. On clusterings-good, bad and spectral , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[12] Santosh S. Vempala,et al. Latent Semantic Indexing , 2000, PODS 2000.
[13] Andrew B. Kahng,et al. Spectral Partitioning with Multiple Eigenvectors , 1999, Discret. Appl. Math..
[14] Alan M. Frieze,et al. Clustering in large graphs and matrices , 1999, SODA '99.
[15] Frank Thomson Leighton,et al. An approximate max-flow min-cut theorem for uniform multicommodity flow problems with applications to approximation algorithms , 1988, [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science.
[16] G. Stewart. Error and Perturbation Bounds for Subspaces Associated with Certain Eigenvalue Problems , 1973 .
[17] Alan M. Frieze,et al. Fast Monte-Carlo algorithms for finding low-rank approximations , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).
[18] Vijay V. Vazirani,et al. Approximation algorithms for metric facility location and k-Median problems using the primal-dual schema and Lagrangian relaxation , 2001, JACM.
[19] Santosh S. Vempala,et al. Latent semantic indexing: a probabilistic analysis , 1998, PODS '98.
[20] A. Frieze,et al. A simple heuristic for the p-centre problem , 1985 .
[21] Inderjit S. Dhillon,et al. Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.
[22] Vijay V. Vazirani,et al. Primal-dual approximation algorithms for metric facility location and k-median problems , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).
[23] V VaziraniVijay,et al. Approximation algorithms for metric facility location and k-Median problems using the primal-dual schema and Lagrangian relaxation , 2001 .
[24] David S. Johnson,et al. Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .
[25] Piotr Indyk. A sublinear time approximation scheme for clustering in metric spaces , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).
[26] Sudipto Guha,et al. A constant-factor approximation algorithm for the k-median problem (extended abstract) , 1999, STOC '99.