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Implementing Data Mining in a DBMS

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Advances in Databases (BNCOD 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2405))

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Abstract

We have developed a clustering algorithm called CLIMIS to demonstrate the advantages of implementing a data mining algorithm in a database management system (DBMS). CLIMIS clusters data held in a DBMS, stores the resulting clusters in the DBMS and executes inside the DBMS. By tightly coupling CLIMIS with the database environment the algorithm scales better to large databases. This is achieved through an index-like structure that uses the database to overcome memory limitations. We further improve the performance of the algorithm by using a technique called adaptive clustering, which controls the size of the clusters.

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References

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  5. Zhang T., Ramakrishnan R., Livny M., 1996, BIRCH: An Efficient Data Clustering Method for Very Large Databases, in Proceedings-ACM-SIGMOD International Conference on Management of Data, Montreal, Canada, pp. 103–114.

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Author information

Authors and Affiliations

  1. Department of Computing, Bournemouth University, Bournemouth, UK

    Konstantina Lepinioti & Stephen McKearney

Authors
  1. Konstantina Lepinioti
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  2. Stephen McKearney
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Editor information

Editors and Affiliations

  1. Department of Information Studies, University of Sheffield, Sheffield, S10 2TN, UK

    Barry Eaglestone

  2. Department of Computer Science, University of Sheffield, Sheffield, S10 2TN, UK

    Siobhán North

  3. Computer Science and Information Systems, Birkbeck College, Malet Street, London, WC1E 7HX, UK

    Alexandra Poulovassilis

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© 2002 Springer-Verlag Berlin Heidelberg

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Lepinioti, K., McKearney, S. (2002). Implementing Data Mining in a DBMS. In: Eaglestone, B., North, S., Poulovassilis, A. (eds) Advances in Databases. BNCOD 2002. Lecture Notes in Computer Science, vol 2405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45495-0_11

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  • DOI: https://doi.org/10.1007/3-540-45495-0_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43905-9

  • Online ISBN: 978-3-540-45495-3

  • eBook Packages: Springer Book Archive

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