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SDM
2003
SIAM
184views Data Mining» more  SDM 2003»
14 years 3 days ago
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
Levent Ertöz, Michael Steinbach, Vipin Kumar
JSA
2000
115views more  JSA 2000»
13 years 10 months ago
Scheduling optimization through iterative refinement
Scheduling DAGs with communication times is the theoretical basis for achieving efficient parallelism on distributed memory systems. We generalize Graham's task-level in a ma...
Mayez A. Al-Mouhamed, Adel Al-Massarani
CORR
2010
Springer
178views Education» more  CORR 2010»
13 years 9 months ago
Enumerative Algorithms for the Shortest and Closest Lattice Vector Problems in Any Norm via M-Ellipsoid Coverings
We give an algorithm for solving the exact Shortest Vector Problem in n-dimensional lattices, in any norm, in deterministic 2O(n) time (and space), given poly(n)-sized advice that...
Daniel Dadush, Chris Peikert, Santosh Vempala
KDD
2009
ACM
611views Data Mining» more  KDD 2009»
14 years 11 months ago
Fast approximate spectral clustering
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-s...
Donghui Yan, Ling Huang, Michael I. Jordan
IPSN
2004
Springer
14 years 4 months ago
Nonparametric belief propagation for self-calibration in sensor networks
Automatic self-calibration of ad-hoc sensor networks is a critical need for their use in military or civilian applications. In general, self-calibration involves the combination o...
Alexander T. Ihler, John W. Fisher III, Randolph L...