A wordification approach to relational data mining

Abstract

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. Wordification aims at producing simple, easy to understand features, acting as words in the transformed Bag-Of-Words representation. As in other 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. The paper presents the wordification methodology, implemented in a cloud-based web data mining platform Clowd-Flows, and describes the experiments in two real-life datasets together with a critical comparison to the RSD propositionalization approach. © 2013 Springer-Verlag.