Cluster-based concept invention for statistical relational learning

We use clustering to derive new relations which augment database schema used in automatic generation of predictive features in statistical relational learning. Clustering improves scalability through dimensionality reduction. More importantly, entities derived from clusters increase the expressivity of feature spaces by creating new first-class concepts which contribute to the creation of new features. For example, in CiteSeer, papers can be clustered based on words or citations giving "topics", and authors can be clustered based on documents they co-author giving "communities". Such cluster-derived concepts become part of more complex feature expressions. Out of the large number of generated features, those which improve predictive accuracy are kept in the model, as decided by statistical feature selection criteria. We present results demonstrating improved accuracy and scalability when predicting publication venues using CiteSeer data.

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Alexandrin Popescul, Lyle H. Ungar
• Keywords: Complex Feature Expressions, Data Mining, KDD 2004, Predictive Accuracy, Statistical Feature Selection