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PPOPP
2005
ACM
15 years 3 months ago
A sampling-based framework for parallel data mining
The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data minin...
Shengnan Cong, Jiawei Han, Jay Hoeflinger, David A...
PPL
2010
117views more  PPL 2010»
14 years 4 months ago
Neighborhood Structures for GPU-Based Local Search Algorithms
Local search (LS) algorithms are among the most powerful techniques for solving computationally hard problems in combinatorial optimization. These algorithms could be viewed as &q...
Thé Van Luong, Nouredine Melab, El-Ghazali ...
FOCS
2006
IEEE
15 years 3 months ago
Approximation algorithms for allocation problems: Improving the factor of 1 - 1/e
Combinatorial allocation problems require allocating items to players in a way that maximizes the total utility. Two such problems received attention recently, and were addressed ...
Uriel Feige, Jan Vondrák
CISIS
2011
IEEE
13 years 9 months ago
Improving Scheduling Techniques in Heterogeneous Systems with Dynamic, On-Line Optimisations
—Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive ap...
Marcin Bogdanski, Peter R. Lewis, Tobias Becker, X...
CPE
1998
Springer
80views Hardware» more  CPE 1998»
15 years 2 months ago
Probability, Parallelism and the State Space Exploration Problem
We present a new dynamic probabilistic state exploration algorithm based on hash compaction. Our method has a low state omission probability and low memory usage that is independen...
William J. Knottenbelt, Mark Mestern, Peter G. Har...