Mining Relational Association Rules for Propositional Classification

In traditional classification setting, training data are represented as a single table, where each row corresponds to an example and each column to a predictor variable or the target variable. However, this propositional (featurebased) representation is quite restrictive when data are organized into several tables of a database. In principle, relational data can be transformed into propositional one by constructing propositional features and performing classification according to some robust and well-known propositional classification methods. Since propositional features should capture relational properties of examples, multi-relational association rules can be adopted in feature construction. Propositionalisation based on relational association rules discovery is implemented in a relational classification framework, named MSRC, tightly integrated with a relational database. It performs the classification at different granularity levels and takes advantage from domain specific knowled...

• Type: Conference
• Year: 2005
• Where: AIIA
• Authors: Annalisa Appice, Michelangelo Ceci, Donato Malerba
• Keywords: AIIA 2005, Artificial Intelligence, Propositional, Propositional Features, Well-known Propositional Classification