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.
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Perovšek, M., Vavpetič, A., Cestnik, B., Lavrač, N. (2013). A Wordification Approach to Relational Data Mining. In: Fürnkranz, J., Hüllermeier, E., Higuchi, T. (eds) Discovery Science. DS 2013. Lecture Notes in Computer Science(), vol 8140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40897-7_10
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DOI: https://doi.org/10.1007/978-3-642-40897-7_10
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