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Database Support for Data Mining Patterns

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Advances in Informatics (PCI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3746))

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Abstract

The need of extracting useful knowledge from large collections of data has led to a great development of data mining systems and techniques. The results of data mining are known as patterns. Patterns can also be found in other scientific areas, such as biology, astronomy, mathematics etc. Today requirements impose the need for a system that efficiently manipulates complex and diverse patterns. In this work, we study the problem of the efficient representation and storage of patterns in a so-called pattern-base Management System. Towards this aim we examine three well known models from the database domain, the relational, the object-relational and the semi-structured (XML) model. The three alternative models are presented and compared based on criteria like generality, extensibility and querying effectiveness. The comparison shows that the semi-structure representation is more appropriate for a pattern-base.

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References

  1. CINQ (Consortium on Discovering Knowledge with Inductive Queries), http://www.cinq-project.org

  2. CWM (Common Warehouse Model), http://www.omg.org/cwm

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  5. Java Data Mining API, http://www.jcp.org/jsr/detail/73.prt

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© 2005 Springer-Verlag Berlin Heidelberg

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Kotsifakos, E., Ntoutsi, I., Theodoridis, Y. (2005). Database Support for Data Mining Patterns. In: Bozanis, P., Houstis, E.N. (eds) Advances in Informatics. PCI 2005. Lecture Notes in Computer Science, vol 3746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573036_2

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  • DOI: https://doi.org/10.1007/11573036_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29673-7

  • Online ISBN: 978-3-540-32091-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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