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Logical Languages for Data Mining

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Logics for Emerging Applications of Databases

Abstract

Data mining focuses on the development of methods and algorithms for such tasks as classification, clustering, rule induction, and discovery of associations. In the database field, the view of data mining as advanced querying has recently stimulated much research into the development of data mining query languages. In the field of machine learning, inductive logic programming has broadened its scope toward extending standard data mining tasks from the usual attribute-value setting to a multirelational setting. After a concise description of data mining, the contribution of logic to both fields is discussed. At the end, we indicate the potential use of logic for unifying different existing data mining formalisms.

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Authors and Affiliations

  1. ISTI, Institute of CNR, Via Moruzzi 1, 56124, Pisa, Italy

    Fosca Giannotti

  2. ICAR, Institute of CNR, Via Bucci 41c, 87036, Rende (CS), Italy

    Giuseppe Manco

  3. Institut d’Informatique, University of Mons-Hainaut, Avenue du Champ de Mars 6, B-7000, Mons, Belgium

    Jef Wijsen

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  1. Fosca Giannotti
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  2. Giuseppe Manco
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  3. Jef Wijsen
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Editors and Affiliations

  1. Dept. of Computer Science and Engineering University at Buffalo, State University of New York, 201 Bell Hall, Buffalo, NY, 14260, USA

    Jan Chomicki

  2. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Ron van der Meyden

  3. Fakultät für Informatik, Otto-von-Guericke-Universität Magdeburg, Postfach 4120, 39016, Magdeburg, Germany

    Gunter Saake

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Giannotti, F., Manco, G., Wijsen, J. (2004). Logical Languages for Data Mining. In: Chomicki, J., van der Meyden, R., Saake, G. (eds) Logics for Emerging Applications of Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18690-5_9

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