Cluster-based Concept Invention for Statistical Relational Learning
Files
Penn collection
Degree type
Discipline
Subject
Algorithms
Relational Learning
Clustering
Feature Generation
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Contributor
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.
Advisor
Date of presentation
Conference name
Conference dates
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
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