Departmental Papers (CIS)
Date of this Version
August 2004
Document Type
Conference Paper
Recommended Citation
Alexandrin Popescul and Lyle H. Ungar, "Cluster-based Concept Invention for Statistical Relational Learning", . August 2004.
Abstract
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.
Keywords
Artificial Intelligence, Algorithms, Relational Learning, Clustering, Feature Generation
Date Posted: 15 May 2005
This document has been peer reviewed.
Comments
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: http://doi.acm.org/10.1145/1014052.1014137