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» An ICA algorithm for analyzing multiple data sets
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KDD
2007
ACM
179views Data Mining» more  KDD 2007»
14 years 2 months ago
Mining statistically important equivalence classes and delta-discriminative emerging patterns
The support-confidence framework is the most common measure used in itemset mining algorithms, for its antimonotonicity that effectively simplifies the search lattice. This com...
Jinyan Li, Guimei Liu, Limsoon Wong
HAIS
2009
Springer
14 years 24 days ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...
ICML
2005
IEEE
14 years 9 months ago
Using additive expert ensembles to cope with concept drift
We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence ...
Jeremy Z. Kolter, Marcus A. Maloof
EWSN
2008
Springer
14 years 7 months ago
Efficient Clustering for Improving Network Performance in Wireless Sensor Networks
Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Mo...
Tal Anker, Danny Bickson, Danny Dolev, Bracha Hod
BMCBI
2006
163views more  BMCBI 2006»
13 years 8 months ago
Ensemble attribute profile clustering: discovering and characterizing groups of genes with similar patterns of biological featur
Background: Ensemble attribute profile clustering is a novel, text-based strategy for analyzing a userdefined list of genes and/or proteins. The strategy exploits annotation data ...
J. R. Semeiks, A. Rizki, Mina J. Bissell, I. Saira...