document: 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 . 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 on two tasks , venue prediction and link prediction , using CiteSeer data .
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