Flexible propositionalization of continuous attributes in relational data mining

In a relational database, data are stored in primary and secondary tables. Propositionalization can transform a relational database into a single attribute-value table, and hence becomes a useful technique for mining relational databases. However, most of the existing propositionalization approaches deal with categorical attributes, and cannot handle a threshold on an attribute and a threshold on the number of objects satisfying the condition on the attribute at the same time. In this paper, we propose a new propositionalization technique called Cardinalization to solve these problems. In order to handle relative numbers, we propose a second variant of our approach called Quantiles which can discretize the cardinality of Cardinalization and achieve a fixed number of features. Therefore, the Quantiles method can be tuned to different deployment contexts. Additionally, we often observe that the best combination of propositionalization and classification methods depends on the new context (e.g., online/incremental learning). One effective solution could be to predict the optimal combination at training time and use it in different deployment contexts. Here we also propose an effective wrapping algorithm, called WPC (Wrapper to combine Propositionalizer and Classifier) to select the best combination of propositionalization and classification methods to address this task. Extensive performance analyses in synthetic and real-life datasets show that our approach is very effective and efficient in relational data mining.