Probabilistic Relational Models with clustering uncertainty

Many machine learning algorithms aim at finding pattern in propositional data, where individuals are all supposed i.i.d. However, the massive usage of relational databases makes multi-relational datasets widespread, and the i.i.d. assumptions are often not reasonable in such data, thus requiring dedicated algorithms. Accurate and efficient learning in such datasets is an important challenge with multiples applications including collective classification and link prediction. Probabilistic Relational Models (PRM) are directed lifted graphical models which generalize Bayesian networks in the relational setting. In this paper, we propose a new PRM extension, named PRM with clustering uncertainty, which overcomes several limitations of PRM with reference uncertainty (PRM-RU) extension, such as the possibility to reason about some individual's cluster membership and use co-clustering to improve association variable dependencies. We also propose a structure learning algorithm for these models and show that these improvements allow: i) better prediction results compared to PRM-RU; ii) in less running time.