Research and experiment on Affinity Propagation clustering algorithm

Huan Zhang, Kun Song
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引用次数: 3

Abstract

This paper introduces Affinity Propagation (AP) clustering algorithm, which is intensively researched by some scholars owing to its advantage of fast speed and no need of setting the initial clusters manually. Mainly analyzed the characteristics of Affinity Propagation clustering algorithm at first, and then compared several principle similarity calculating methods based on Euclidean distance and Mahalanobis distance and etc. Experiment on AP clustering algorithm were done with the parts of the UCI data sets, thus the effectiveness of this algorithm was verified. Finally, the experimental results were analyzed in general.
亲和传播聚类算法的研究与实验
本文介绍了亲和性传播(Affinity Propagation, AP)聚类算法,该算法具有速度快、无需手动设置初始聚类等优点,受到了一些学者的广泛研究。首先主要分析了亲和传播聚类算法的特点,然后比较了几种基于欧几里得距离和马氏距离等的基本相似度计算方法。利用部分UCI数据集对AP聚类算法进行了实验,验证了该算法的有效性。最后,对实验结果进行了总体分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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