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» Modeling Relational Data as Graphs for Mining
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133
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ICDM
2010
IEEE
132views Data Mining» more  ICDM 2010»
15 years 2 months ago
Monotone Relabeling in Ordinal Classification
In many applications of data mining we know beforehand that the response variable should be increasing (or decreasing) in the attributes. Such relations between response and attrib...
Ad Feelders
IJAR
2006
89views more  IJAR 2006»
15 years 4 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
SDM
2009
SIAM
164views Data Mining» more  SDM 2009»
16 years 1 months ago
Time-Decayed Correlated Aggregates over Data Streams.
Data stream analysis frequently relies on identifying correlations and posing conditional queries on the data after it has been seen. Correlated aggregates form an important examp...
Graham Cormode, Srikanta Tirthapura, Bojian Xu
ICDM
2003
IEEE
210views Data Mining» more  ICDM 2003»
15 years 10 months ago
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
Hua-Jun Zeng, Xuanhui Wang, Zheng Chen, Hongjun Lu...
140
Voted
SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
15 years 6 months ago
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Albert Bifet, Ricard Gavaldà