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» Maximal Vector Computation in Large Data Sets
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IPPS
2009
IEEE
14 years 2 months ago
A metascalable computing framework for large spatiotemporal-scale atomistic simulations
A metascalable (or “design once, scale on new architectures”) parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials...
Ken-ichi Nomura, Richard Seymour, Weiqiang Wang, H...
ICPR
2008
IEEE
14 years 2 months ago
An approximate algorithm for median graph computation using graph embedding
Graphs are powerful data structures that have many attractive properties for object representation. However, some basic operations are difficult to define and implement, for ins...
Miquel Ferrer, Ernest Valveny, Francesc Serratosa,...
ICDE
2009
IEEE
290views Database» more  ICDE 2009»
14 years 9 months ago
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
Graphs are being increasingly used to model a wide range of scientific data. Such widespread usage of graphs has generated considerable interest in mining patterns from graph datab...
Sayan Ranu, Ambuj K. Singh
CVPR
2011
IEEE
13 years 3 months ago
Hierarchical Semantic Indexing for Large Scale Image Retrieval
This paper addresses the problem of similar image retrieval, especially in the setting of large-scale datasets with millions to billions of images. The core novel contribution is ...
Jia Deng, Alexander Berg, Li Fei-Fei
KDD
2006
ACM
150views Data Mining» more  KDD 2006»
14 years 8 months ago
Maximally informative k-itemsets and their efficient discovery
In this paper we present a new approach to mining binary data. We treat each binary feature (item) as a means of distinguishing two sets of examples. Our interest is in selecting ...
Arno J. Knobbe, Eric K. Y. Ho