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KDD
1998
ACM
106views Data Mining» more  KDD 1998»
13 years 11 months ago
Analysing Rock Samples for the Mars Lander
In the near future NASAintends to explore various regions of our solar systemusing robotic devices such as rovers, spacecraft, airplanes, and/or balloons. Such platforms will carr...
Jonathan J. Oliver, Ted Roush, Paul Gazis, Wray L....
PR
2006
164views more  PR 2006»
13 years 7 months ago
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the...
Hong Chang, Dit-Yan Yeung
ICDE
2007
IEEE
165views Database» more  ICDE 2007»
14 years 9 months ago
On Randomization, Public Information and the Curse of Dimensionality
A key method for privacy preserving data mining is that of randomization. Unlike k-anonymity, this technique does not include public information in the underlying assumptions. In ...
Charu C. Aggarwal
KDD
2005
ACM
160views Data Mining» more  KDD 2005»
14 years 8 months ago
Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering
Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two t...
Bin Gao, Tie-Yan Liu, Xin Zheng, QianSheng Cheng, ...
STOC
2007
ACM
112views Algorithms» more  STOC 2007»
14 years 7 months ago
Smooth sensitivity and sampling in private data analysis
We introduce a new, generic framework for private data analysis. The goal of private data analysis is to release aggregate information about a data set while protecting the privac...
Kobbi Nissim, Sofya Raskhodnikova, Adam Smith