• unsupervised ensemble minority clustering

    نویسندگان :
    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1013
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     cluster analysis lies at the core of most unsupervised learning tasks. however, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise.the approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. in supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms.in this work, we propose a novel ensemble minority clustering algorithm, ewocs, suitable for weak clustering combination. its properties have been theoretically proved under a loose set of constraints. we also propose a number of weak clustering algorithms, and an unsupervised procedure to determine the scaling parameters for gaussian kernels used within the task.we have implemented a number of approaches built from the proposed components, and evaluated them on a collection of datasets. the results show how approaches based on ewocs are competitive with respect to—and even outperform—other minority clustering approaches in the state of the art.

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