Mining tree patterns with almost smallest supertrees

J. Knijf, de

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    2 Citations (Scopus)


    In this work we describe a new algorithm to mine tree structured data. Our method computes an almost smallest supertree, based upon iteratively employing tree alignment. This supertree is a global pattern, that can be used both for descriptive and predictive data mining tasks. Experiments performed on two real datasets, show that our approach leads to a drastic compression of the database. Furthermore, when the resulting pattern is used for classification, the results show a considerable improvement over existing algorithms.Moreover, the incremental nature of the algorithm provides a flexible way of dealing with extension or reduction of the original dataset. Finally, the computation of the almost smallest supertree can be easily parallelized.
    Original languageEnglish
    Title of host publicationProceedings of teh SIAM International Conference on Data Mining (SDM 2008, Atlanta GA, USA, April 24-26, 2008)
    PublisherSociety for Industrial and Applied Mathematics (SIAM)
    Publication statusPublished - 2008


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