S. Doddi, M.V. Marathe, S. S. Ravi, D. S. Taylor, and P. Widmayer. Approximation algorithms for clustering to minimize the sum of diameters. Nordic Journal of Computing, 7(3):185, Fall 2000. Home/Search Document Details and Download
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A Min-max Cut Algorithm for Graph Partitioning and Data.. - Ding, He, Zha, Gu, Simon (2001) (3 citations) (Correct)
....vector using a perturbation analysis on the (normalized) Laplacian matrix [6] This further strengthens the connection between objective function and the Fiedler vector. Beside spectral partitioning methods, other recent partitioning methods seek to minimize the sum of subgraph diameters, see [7] or k center problem [1] for examples. There are other clustering methods that use singular value decompositions, for example [10] 4 Random graph model Perhaps the most important feature of the Mcut method is that it tends to produce balanced cut, i.e. the resulting clusters (subgraphs) have ....
S. Doddi, M.V. Marathe, S. S. Ravi, D. S. Taylor, and P. Widmayer. Approximation algorithms for clustering to minimize the sum of diameters. Nordic Journal of Computing, 7(3):185, Fall 2000.
Computational Learning Theory - Principle Investigators And Self-citation (Marathe) (Correct)
....lead to an explosion in the computational resources required are pervasive. The recent field of Computational Learning Theory (COLT) 1 has provided a resolution to this historical dilemma by posing a computationally 1 The foundations of COLT were laid in the two seminal papers by Valiant in 1984, where he introduced the probably approximately correct (PAC) model of learning. 1 D E F: High Performance Computing F 1 0 0 1 A: Computer Security First Principles Knowledge B: Genomics C: Weapons Design E: Nonproliferation A B C COLT learnable D: Simulation Science ....
....for a number of important problems encountered LANL. 2 Background Although the problem of building models from empirical data can be traced back to (at least) Gauss, this field entered a new era with the introduction of the Probably Approximately Correct (PAC) learning model by Valiant in 1984. This model provided a more rigorous framework in 2 which to study this problem. This enrichment was due in large part to the integration of Statistical Learning Theory (the design of predictors from sample data) and the Theory of Computation. The time for integration was ripe. Advances in ....
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S. Doddi, M.V. Marathe, S.S. Ravi D. Taylor and P. Widmayer, "Approximation algorithms for clustering to minimize the sum of diameters," to appear in Proc. 7th Scandinavian Workshop on Algorithm Theory, (SWAT), July, 2000, Bergen, Norway.
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