Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Relational Data Mining
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Propositionalization approaches to relational data mining
Relational Data Mining
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
IBC: A First-Order Bayesian Classifier
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A Genetic Algorithm for Propositionalization
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
An assessment of submissions made to the predictive toxicology evaluation challenge
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
1BC2: a true first-order Bayesian classifier
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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Data is mainly available in relational formats, so relational data mining receives a lot of interest. Propositionalisation consists in changing the representation of relational data in order to apply usual attribute-value learning systems. Data mining practitioners are not necessarily aware of existing works and try to propositionalise by hand. Unfortunately there exists some tempting pitfalls. This article aims at bridging the gap between data mining practitioners and relational data, pointing out the most usual traps and proposing correct approaches to propositionalisation. Similar situations with sequential data and the multiple-instance problem are also covered. Finally the strengths and weaknesses of propositionalisation are listed.