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...
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
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...
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...
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...