Abstract
Knowledge discovery in databases (KDD) plays an important role in decision-making tasks by supporting end users both in exploring and understanding of very large datasets and in building predictive models with validity over unseen data. KDD is an ad-hoc, iterative process comprising tasks that range from data understanding and preparation to model building and deployment. Support for KDD should, therefore, be founded on a closure property, i.e., the ability to compose tasks seamlessly by taking the output of a task as the input of another. Despite some recent progress, KDD is still not as conveniently supported as end users have reason to expect due to three major problems: (1) lack of task compositionality, (2) undue dependency on user expertise, and (3) lack of generality. This paper contributes to ameliorate these problems by proposing an abstract algebra for KDD, called K-algebra, whose underlying data model and primitive operations accommodate a wide range of KDD tasks. Such an algebra is a necessary step towards the development of optimisation techniques and efficient evaluation that would, in turn, pave the way for the development of declarative, surface KDD languages without which end-user support will remain less than convenient, thereby damaging the prospects for mainstream acceptance of KDD technology.
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Gerber, L., Fernandes, A.A.A. (2004). An Abstract Algebra for Knowledge Discovery in Databases. In: Benczúr, A., Demetrovics, J., Gottlob, G. (eds) Advances in Databases and Information Systems. ADBIS 2004. Lecture Notes in Computer Science, vol 3255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30204-9_6
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DOI: https://doi.org/10.1007/978-3-540-30204-9_6
Publisher Name: Springer, Berlin, Heidelberg
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