Abstract
We present a logical framework for querying inductive databases, which can accommodate a variety of data mining tasks, such as classification, clustering, finding frequent patterns and outliers detection. We also address the important issues of the expressive power of inductive query languages. We show that the proposed logic programming paradigm has equivalent expressive power to an algebra for data mining presented in the literature [1].
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Liu, HC., Vincent, M., Liu, J., Li, J. (2014). Logics for Representing Data Mining Tasks in Inductive Databases. In: Wang, H., Sharaf, M.A. (eds) Databases Theory and Applications. ADC 2014. Lecture Notes in Computer Science, vol 8506. Springer, Cham. https://doi.org/10.1007/978-3-319-08608-8_20
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DOI: https://doi.org/10.1007/978-3-319-08608-8_20
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