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» The Stardinates - Visualizing Highly Structured Data
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146
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GRC
2008
IEEE
15 years 4 months ago
Neighborhood Smoothing Embedding for Noisy Manifold Learning
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Guisheng Chen, Junsong Yin, Deyi Li
116
Voted
ICDM
2008
IEEE
95views Data Mining» more  ICDM 2008»
15 years 10 months ago
Publishing Sensitive Transactions for Itemset Utility
We consider the problem of publishing sensitive transaction data with privacy preservation. High dimensionality of transaction data poses unique challenges on data privacy and dat...
Yabo Xu, Benjamin C. M. Fung, Ke Wang, Ada Wai-Che...
133
Voted
SIGMOD
2002
ACM
129views Database» more  SIGMOD 2002»
16 years 3 months ago
Dwarf: shrinking the PetaCube
Dwarf is a highly compressed structure for computing, storing, and querying data cubes. Dwarf identifies prefix and suffix structural redundancies and factors them out by coalesci...
Yannis Sismanis, Antonios Deligiannakis, Nick Rous...
JODL
2000
123views more  JODL 2000»
15 years 3 months ago
On Z39.50 wrapping and description logics
Z39.50 is a client/server protocol widely used in digital libraries and museums for searching and retrieving information spread over a number of heterogeneous sources. To overcome ...
Yannis Velegrakis, Vassilis Christophides, Panos C...
142
Voted
IJAR
2010
130views more  IJAR 2010»
15 years 2 months ago
Learning locally minimax optimal Bayesian networks
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Tomi Silander, Teemu Roos, Petri Myllymäki