Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Simple Estimators for Relational Bayesian Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on 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
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
CrossMine: Efficient Classification Across Multiple Database Relations
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
An efficient multi-relational Naïve Bayesian classifier based on semantic relationship graph
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Classification spanning correlated data streams
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Generalized hypertree decompositions: np-hardness and tractable variants
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Algorithms for acyclic database schemes
VLDB '81 Proceedings of the seventh international conference on Very Large Data Bases - Volume 7
An Efficient Relational Decision Tree Classification Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Top-down induction of first-order logical decision trees
Artificial Intelligence
1BC2: a true first-order Bayesian classifier
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Multi-relational classification is a mining method aiming at building classifiers for the tuples in some target relation based on its own data as well as on the data possibly dispersed over other non-target relations, by exploiting the relationships among them formalized via foreign key constraints. While improving on the efficacy of the resulting classifiers, propagating data via the foreign key constraints deteriorates the scalability of the underlying algorithm. In the paper, various techniques are discussed to efficiently implement this propagation task, and hence to boost performances of current multi-relational classification algorithms. These techniques are based on suitable adaptations of state-of-the-art query optimization methods, and are conceived to be coupled with database management systems. A system prototype integrating all the techniques is illustrated, and results of experimental activity conducted on top of it are eventually discussed.