Multirelational classification: a multiple view approach

Multirelational classification aims at discovering useful patterns across multiple inter-connected tables (relations) in a relational database. Many traditional learning techniques, however, assume a single table or a flat file as input (the so-called propositional algorithms). Existing multirelational classification approaches either "upgrade" mature propositional learning methods to deal with relational presentation or extensively "flatten" multiple tables into a single flat file, which is then solved by propositional algorithms. This article reports a multiple view strategy--where neither "upgrading" nor "flattening" is required--for mining in relational databases. Our approach learns from multiple views (feature set) of a relational databases, and then integrates the information acquired by individual view learners to construct a final model. Our empirical studies show that the method compares well in comparison with the classifiers induced b...

• Type: Journal
• Year: 2008
• Where: KAIS
• Authors: Hongyu Guo, Herna L. Viktor
• Keywords: KAIS 2008, Multirelational Classification, Propositional Algorithms, Relational Databases