ECML '93 Proceedings of the European Conference on Machine Learning
Involving Aggregate Functions in Multi-relational Search
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Aggregation-based feature invention and relational concept classes
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Relational mining in spatial domains: accomplishments and challenges
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Boosting tuple propagation in multi-relational classification
Proceedings of the 15th Symposium on International Database Engineering & Applications
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
Simple decision forests for multi-relational classification
Decision Support Systems
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
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This paper presents a novel method for multi-relational classification via an aggregation-based Inductive Logic Programming (ILP) approach. We extend the classical ILP representation by aggregation of multiple-features which aid the classification process by allowing for the analysis of relationships and dependencies between different features. In order to efficiently learn rules of this rich format, we present a novel algorithm capable of performing aggregation with the use of virtual joins of the data. By using more expressive aggregation predicates than the existential quantifier used in standard ILP methods, we improve the accuracy of multi-relational classification. This claim is supported by experimental evaluation on three different real world datasets.