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KDD
2004
ACM
139views Data Mining» more  KDD 2004»
14 years 7 months ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
14 years 7 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
INFOCOM
2010
IEEE
13 years 5 months ago
Tracking Quantiles of Network Data Streams with Dynamic Operations
— Quantiles are very useful in characterizing the data distribution of an evolving dataset in the process of data mining or network monitoring. The method of Stochastic Approxima...
Jin Cao, Li (Erran) Li, Aiyou Chen, Tian Bu

Publication
244views
15 years 7 months ago
Phenomenon-aware Stream Query Processing
Spatio-temporal data streams that are generated from mobile stream sources (e.g., mobile sensors) experience similar environmental conditions that result in distinct phenomena. Sev...
M. H. Ali, Mohamed F. Mokbel, Walid G. Aref
SIGMOD
2006
ACM
131views Database» more  SIGMOD 2006»
14 years 7 months ago
An automatic construction and organization strategy for ensemble learning on data streams
As data streams are gaining prominence in a growing number of emerging application domains, classification on data streams is becoming an active research area. Currently, the typi...
Yi Zhang, Xiaoming Jin