Departmental Papers (CIS)

Date of this Version

August 2004

Document Type

Conference Paper


Postprint version. Copyright ACM, 2004. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD 2004), pages 665-670.
Publisher URL:


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.


Artificial Intelligence, Algorithms, Relational Learning, Clustering, Feature Generation



Date Posted: 15 May 2005

This document has been peer reviewed.