联机核模糊C均值聚类方法

基于核模糊C均值(kernel fuzzy C-means， KFCM)提出了一种针对较大规模数据的联机核模糊C均值 (online kernel fuzzy C-means, OKFCM) 算法，同时考虑到核参数的选择困境，借鉴多核学习思想，进一步衍生出了联机多核模糊C均值 (online multiple kernel fuzzy C-means, OMKFCM) 算法。由此，在有效缓和核参数选择难题的同时，新算法不仅继承了KFCM优越的聚类特性且适合聚类数据流。最后，在人工和真实数据集上验证了新提出的核联机算法比现有基于划分的大规模数据处理算法具有更好的性能。

Online kernel fuzzy C-means clustering algorithm

A new online kernel fuzzy C-means (OKFCM) algorithm for large scale datasets based on kernel fuzzy C-means (KFCM) is proposed. In addition, taking into account the difficulties in selecting kernel parameters, an online multiple kernel fuzzy C-means (OMKFCM) algorithm is derived based on multiple kernel learning methods. Thus, the proposed algorithms not only ease the problem of selecting kernel parameters and inherit the superior clustering performance of the KFCM, but also are suitable for clustering data streams. Finally, the new online kernel algorithms are verified to have a better performance on artificial and real datasets compared with state-of-the-art partition clustering algorithms for large scale datasets.