Aiming at the problem that the initial cluster centers randomly selected in kernel k-means clustering are likely to cause algorithm failure, a method of clustering center determination based on density estimation is proposed. This method uses the idea of kd-tree to choose the point with large probability density and separation as the initial point, and uses the wavelet kernel to describe the nonlinear mapping. Experiments on artificial data sets and real data sets show that the error rate of this method is low and has certain practical significance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Kernel K-means Clustering Algorithm Based on Parzen-window Estimation


    Beteiligte:
    Liang, QIN (Autor:in) / Wenguang, ZHANG (Autor:in) / Zhen, WANG (Autor:in) / Xinhua, NIE (Autor:in) / Xianjun, SHI (Autor:in)


    Erscheinungsdatum :

    2018-08-01


    Format / Umfang :

    124208 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An improved Bayes fusion algorithm with the Parzen window method

    Gang Wang, / De-gan Zhang, / Hai Zhao, | IEEE | 2002


    An Improved Bayes Fusion Algorithm with the Parzen Window Method

    Wang, G. / Zhang, G.-D. / Zhao, H. et al. | British Library Conference Proceedings | 2002


    A Parzen window-based approach to detection of infrared dim target under sea-sky background

    Deng, H. / Yao, H. / Wei, Y. T. et al. | British Library Online Contents | 2016


    Regression Based Bandwidth Selection for Segmentation Using Parzen Windows

    Singh, M. / Ahuja, N. / IEEE | British Library Conference Proceedings | 2003