Set-oriented data mining in relational databases

M.A.W. Houtsma, Arun Swami

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    27 Citations (Scopus)
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    Abstract

    Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented operations. Query optimization technology can then be used for efficient processing. In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build interactive data mining tools for large databases.
    Original languageUndefined
    Article number10.1016/0169-023X(95)00024-M
    Pages (from-to)245-262
    Number of pages18
    JournalData & knowledge engineering
    Volume17
    Issue number3
    DOIs
    Publication statusPublished - Dec 1995

    Keywords

    • DB-DM: DATA MINING
    • IR-66244
    • Data Mining
    • Set-oriented algorithms
    • EWI-6305
    • Optimization
    • METIS-118750
    • IR-18230

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