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CIKM
2006
Springer
13 years 11 months ago
Finding highly correlated pairs efficiently with powerful pruning
We consider the problem of finding highly correlated pairs in a large data set. That is, given a threshold not too small, we wish to report all the pairs of items (or binary attri...
Jian Zhang, Joan Feigenbaum
ITC
1995
IEEE
102views Hardware» more  ITC 1995»
13 years 11 months ago
Visualizing Quality
Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps ...
Solomon Max
AAAI
2007
13 years 9 months ago
Predictive Exploration for Autonomous Science
Often remote investigations use autonomous agents to observe an environment on behalf of absent scientists. Predictive exploration improves these systems’ efficiency with onboa...
David R. Thompson
ALMOB
2007
74views more  ALMOB 2007»
13 years 7 months ago
Evaluating deterministic motif significance measures in protein databases
Background: Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in...
Pedro Gabriel Ferreira, Paulo J. Azevedo
TASLP
2008
143views more  TASLP 2008»
13 years 7 months ago
Strategies to Improve the Robustness of Agglomerative Hierarchical Clustering Under Data Source Variation for Speaker Diarizatio
Many current state-of-the-art speaker diarization systems exploit agglomerative hierarchical clustering (AHC) as their speaker clustering strategy, due to its simple processing str...
K. J. Han, S. Kim, S. S. Narayanan