计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 187-192.doi: 10.11896/j.issn.1002-137X.2017.10.035
夏春梦,倪志伟,倪丽萍,张霖
XIA Chun-meng, NI Zhi-wei, NI Li-ping and ZHANG Lin
摘要： 针对近邻传播聚类算法在构造相似度矩阵时因对多重尺度和任意形状数据敏感而聚类效果不理想的缺陷,提出一种基于密度调整和流形距离的近邻传播算法。该算法将“领域密度”和“流形理论”的思想引入近邻传播算法,利用基于密度调整和流形的距离更好地刻画了样本空间的真实分布状况,解决了相似度矩阵不能充分表示数据之间内在关系的问题,在一定程度上提高了近邻传播聚类算法的聚类效果。通过在人工数据集和标准数据集上进行实验对比,验证了算法的有效性和优越性。
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