A Wordification Approach to Relational Data Mining: Early Results

This paper describes a propositionalization technique called wordification. Wordification is inspired by text mining and can be seen as a transformation of a relational database into a corpus of documents. As in previous propositionalization methods, after the wordification step any propositional data mining algorithm can be applied. The most notable advantage of the presented technique is greater scalability the propositionalization step is done in time linear to the number of attributes times the number of examples for one-to-many databases. Furthermore, wordification results in easily understandable propositional feature representation. We present our initial experiments on two real-life datasets.