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MySQL Data Mining: Extending MySQL to Support Data Mining Primitives (Demo)

  • Conference paper
Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6278))

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Abstract

The development of predictive applications built on top of knowledge bases is rapidly growing, therefore database systems, especially the commercial ones, are boosting with native data mining analytical tools. In this paper, we present an integration of data mining primitives on top of MySQL 5.1. In particular, we extended MySQL to support frequent itemsets computation and classification based on C4.5 decision trees. These commands are recognized by the parser that has been properly extended to support new SQL statements. Moreover, the implemented algorithms were engineered and integrated in the source code of MySQL in order to allow large-scale applications and a fast response time. Finally, a graphical interface guides the user to explore the new data mining facilities.

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Author information

Authors and Affiliations

  1. Dept. of Mathematics and Computer Sciences, University of Catania,  

    Alfredo Ferro, Rosalba Giugno, Piera Laura Puglisi & Alfredo Pulvirenti

Authors
  1. Alfredo Ferro
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  2. Rosalba Giugno
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  3. Piera Laura Puglisi
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  4. Alfredo Pulvirenti
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Editor information

Editors and Affiliations

  1. School of Engineering, Cardiff University, The Parade, CF24 3AA, Cardiff, UK

    Rossitza Setchi

  2. Dept. of Computer Science and Software Engineering, University of Portsmouth, BUckingham Building, Lion Terrace, PO1 3HE, Portsmouth, UK

    Ivan Jordanov

  3. KES International, 145-157 St. John Street, EC1V 4PY, London, UK

    Robert J. Howlett

  4. School of Electrical and Information Engineering, University of South Australia, Adelaide, Mawson Lakes Campus, 5095, SA, Australia

    Lakhmi C. Jain

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© 2010 Springer-Verlag Berlin Heidelberg

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Ferro, A., Giugno, R., Puglisi, P.L., Pulvirenti, A. (2010). MySQL Data Mining: Extending MySQL to Support Data Mining Primitives (Demo). In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15393-8_49

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  • DOI: https://doi.org/10.1007/978-3-642-15393-8_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15392-1

  • Online ISBN: 978-3-642-15393-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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