Results 291 to 300 of about 3,357,696 (317)

Clustering with or without the Approximation

Journal of Combinatorial Optimization, 2010
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Schalekamp, F., Yu, M., Van Zuylen, A.
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Approximate Spectral Clustering

2009
While spectral clustering has recently shown great promise, computational cost makes it infeasible for use with large data sets. To address this computational challenge, this paper considers the problem of approximate spectral clustering, which enables both the feasibility (of approximately clustering in very large and unloadable data sets) and ...
Liang Wang, Christopher Leckie, Kotagiri Ramamohanarao, James Bezdek   +3 more
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Dynamical Cluster Approximation

2011
The dynamical cluster approximation (DCA) is a method which systematically incorporates nonlocal corrections to the dynamical mean-field approximation. Here we present a pedagogical discussion of the DCA by describing it as a Φ-derivable coarse-graining approximation in k-space, which maps an infinite lattice problem onto a periodic finite-sized ...
H. Fotso, S. Yang, K. Chen, S. Pathak, J. Moreno, M. Jarrell, K. Mikelsons, E. Khatami, D. Galanakis   +8 more
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Approximations to Bayesian Clustering Rules

Biometrika, 1981
SUMMARY The amount of computation required for implementing the Bayesian cluster analysis suggested by Binder (1978) is often too large for exact results to be feasible. A general algorithm is proposed for approximating the similarity matrix and the resulting optimal partition. This algorithm is applied to artificial and to real data. For the real data,
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Fast approximate spectral clustering

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-scale problems due to its computational complexity of O(n3) in general, with n the number of data points.
Donghui Yan, Ling Huang, Michael I. Jordan   +2 more
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K-clustered tensor approximation

ACM Transactions on Graphics, 2012
With the increasing demands for photo-realistic image synthesis in real time, we propose a sparse multilinear model, which is named K-Clustered Tensor Approximation (K-CTA), to efficiently analyze and approximate large-scale multidimensional visual datasets, so that both storage space and rendering time are ...
Yu-Ting Tsai, Zen-Chung Shih
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Coupled-cluster approximation in momentum space

Physical Review B, 1994
A simple coupled-cluster-type approximation is introduced, which generalizes in momentum ([ital k]) space a method recently presented in the literature for real space, and tested on the exactly solvable [ital XXZ] anisotropic quantum antiferromagnetic spin-1/2 chain.
, Cornu, , Jolicoeur, , Le Guillou JC
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Momentum-selective metal-insulator transition in the two-dimensional Hubbard model: An 8-site dynamical cluster approximation study

, 2009
Institut de Physique Th´eorique, CEA, IPhT, CNRS, URA 2306, F-91191 Gif-sur-Yvette, France(Dated: March 15th, 2009)Metal-insulator transitions in the paramagnetic phase of the two dimensional square lattice Hub-bard model are studied using the dynamical ...
P. Werner, E. Gull, O. Parcollet, A. Millis   +3 more
semanticscholar   +1 more source

Cluster Gutzwiller Approximation

1991
In recent years, a considerable number of approximation schemes has been suggested for the treatment of lattice models of strongly interacting electron systems. The problem is always the following: how to incorporate strong local correlations into a many-electron state which is, on the whole, itinerant.
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Approximating the Becker–Döring cluster equations

Applied Numerical Mathematics, 2001
The dynamics of to coagulation and fragmentation of identical particle clusters is described by an infinite system of ODE's. The authors propose schemes based on a piecewise constant flux approximation and the Galerkin procedure.
Duncan, Dugald B., Soheili, Ali R.
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