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» Maximal Vector Computation in Large Data Sets
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ADMA
2009
Springer
145views Data Mining» more  ADMA 2009»
14 years 4 months ago
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
The goal of multi-objective clustering (MOC) is to decompose a dataset into similar groups maximizing multiple objectives in parallel. In this paper, we provide a methodology, arch...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricar...
HPCA
1999
IEEE
14 years 1 months ago
Permutation Development Data Layout (PDDL)
Declustered data organizations in disk arrays (RAIDs) achieve less-intrusive reconstruction of data after a disk failure. We present PDDL, a new data layout for declustered disk a...
Thomas J. E. Schwarz, Jesse Steinberg, Walter A. B...
JMLR
2010
162views more  JMLR 2010»
13 years 4 months ago
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging ...
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom D...
INDIASE
2009
ACM
14 years 3 months ago
Computing dynamic clusters
When trying to reverse engineer software, execution trace analysis is increasingly used. Though, by using this technique we are quickly faced with an enormous amount of data that ...
Philippe Dugerdil, Sebastien Jossi
ICCV
2007
IEEE
14 years 11 months ago
Semi-supervised Discriminant Analysis
Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. The projection vectors are commonly obtained by maximizing ...
Deng Cai, Xiaofei He, Jiawei Han