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    Scalable mining for classification rules in relational databases

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    Vitter_2004.pdf (434.2Kb)
    Issue Date
    2004
    Author
    Wang, Min
    Iyer, Bala
    Vitter, Jeffrey Scott
    Publisher
    Institute of Mathematical Statistics
    Type
    Article
    Article Version
    Scholarly/refereed, publisher version
    Metadata
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    Abstract
    Data mining is a process of discovering useful patterns (knowledge) hidden in extremely large datasets. Classification is a fundamental data mining function, and some other functions can be reduced to it. In this paper we propose a novel classification algorithm (classifier) called MIND (MINing in Databases). MIND can be phrased in such a way that its implementation is very easy using the extended relational calculus SQL, and this in turn allows the classifier to be built into a relational database system directly. MIND is truly scalable with respect to I/O efficiency, which is important since scalability is a key requirement for any data mining algorithm. We have built a prototype of MIND in the relational database management system DB2 and have benchmarked its performance. We describe the working prototype and report the measured performance with respect to the previous method of choice. MIND scales not only with the size of datasets but also with the number of processors on an IBM SP2 computer system. Even on uniprocessors, MIND scales well beyond dataset sizes previously published for classifiers.We also give some insights that may have an impact on the evolution of the extended relational calculus SQL.
    Description
    doi:10.1214/lnms/1196285404
    URI
    http://hdl.handle.net/1808/7167
    DOI
    https://doi.org/10.1214/lnms/1196285404
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    • Distinguished Professors Scholarly Works [918]
    • Electrical Engineering and Computer Science Scholarly Works [302]
    • Provost Office Published Articles [95]
    Citation
    M. Wang, B. Iyer, and J. S. Vitter. “Scalable Mining for Classification Rules in Relational Databases,” Herman Rubin Festschrift, Lecture Notes Monograph Series, 45, Institute of Mathematical Statistics, Hayward, CA, Fall 2004. An extended abstract appears in Proceedings of the International Database Engineering & Application Symposium (IDEAS ’98), Cardiff, Wales, July 1998, 58–67. A shorter version appears in Proceedings of the ACM SIGMOD Data Mining and Knowledge Discovery Workshop (DMKD ’98), Seattle, WA, June 1998. http://dx.doi.org/10.1214/lnms/1196285404

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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