计算机科学 ›› 2015, Vol. 42 ›› Issue (1): 232-235.doi: 10.11896/j.issn.1002-137X.2015.01.051
甘月松,陈秀宏,陈晓晖
GAN Yue-song, CHEN Xiu-hong and CHEN Xiao-hui
摘要： Affinity Propagation(AP)聚类算法将所有数据点作为潜在的聚类中心,在相似度矩阵的基础上通过消息传递进行聚类。与传统聚类方法相比,对于大规模数据集,AP是一种快速、有效的聚类方法。但是AP算法在聚类结构复杂的(非团状)数据集上得到的效果并不是很好。因此,在AP的基础上加入一个merge过程,将AP算法改进为M-AP算法,可以有效地解决这种问题。而当样本数目比较大时,将CVM压缩算法融入其中,可以有效地解决大样本问题。
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