Algorithmic Program DeBugging
Relational Data Mining
Distance based approaches to relational learning and clustering
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Clustering and Identifying Temporal Trends in Document Databases
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical learning from relational databases
Statistical learning from relational databases
Streaming feature selection using alpha-investing
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Leveraging relational autocorrelation with latent group models
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Leveraging Relational Autocorrelation with Latent Group Models
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Linear prediction models with graph regularization for web-page categorization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Change of representation for statistical relational learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Feature enrichment and selection for transductive classification on networked data
Pattern Recognition Letters
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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.