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Research and Experiment on Affinity Propagation Clustering Algorithm

机译:关联传播聚类算法研究与实验

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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 w as verified. Finally, the experimental results were analyzed in general.
机译:本文介绍了亲和力传播(AP)聚类算法,由于其优势快速速度的优势,并且不需要手动设置初始集群的优势,这是集中化的。主要分析了亲和传播聚类算法的特征首先,比较了基于欧几里德距离和Mahalanobis距离的几个原理相似性计算方法等。AP聚类算法的实验是用UCI数据集的部件完成的,从而实现了效果该算法W如验证。最后,一般分析了实验结果。

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