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分层AP聚类算法

于 2022-07-14 发布 文件大小:7.62 kB
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近邻传播(Affinity Propagation,AP)聚类具有不需要设定聚类个数、快速准确的优点,但无法适应于大规模数据的应用需求.针对此问题,提出了分层近邻传播聚类算 法.首先,将待聚类数据集划分为若干适合AP算法高效执行的子集,分别推举出各个子集的聚类中心;然后对所有子集聚类中心再次执行AP聚类,推举出整个数 据集的全局聚类中心;最后根据与这些全局聚类中心的相似度对聚类样本进行划分,从而实现对大规模数据的高效聚类.在真实和模拟数据集上的实验结果均表明, 与AP聚类和自适应AP聚类相比,该方法在保证较好聚类效果的同时,极大地降低了聚类的时间消耗.

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