A Comparative Study of Some Clustering Algorithms on Shape Data

IF 0.1 Q4 STATISTICS & PROBABILITY JIRSS-Journal of the Iranian Statistical Society Pub Date : 2021-12-01 DOI:10.52547/jirss.20.2.29
Sahar Asili, A. Mohammadpour, O. Naghshineh Arjmand, M. Golalizazdedh
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引用次数: 1

Abstract

. Recently, some statistical studies have been done using the shape data. One of these studies is clustering shape data, which is the main topic of this paper. We are going to study some clustering algorithms on shape data and then introduce the best algorithm based on accuracy, speed, and scalability criteria. In addition, we propose a method for representing the shape data that facilitates and speeds up the shape clustering algorithms. Although the mentioned method is not very accurate, it is fast; therefore, it is useful for datasets with a high number of landmarks or observations, which take a long time to be clustered by means of other algorithms. It should be noted that this method is not new, but in this article we apply it in shape data analysis. clustering algorithms on five shape datasets.
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几种形状数据聚类算法的比较研究
最近,已经使用形状数据进行了一些统计研究。其中一项研究是对形状数据进行聚类,这是本文的主要主题。我们将研究一些形状数据的聚类算法,然后介绍基于准确性、速度和可扩展性标准的最佳算法。此外,我们还提出了一种表示形状数据的方法,该方法方便并加快了形状聚类算法。虽然上述方法不是很准确,但速度很快;因此,它适用于具有大量地标或观测值的数据集,这些数据集需要很长时间才能通过其他算法进行聚类。需要注意的是,这种方法并不新鲜,但在本文中,我们将其应用于形状数据分析。五个形状数据集上的聚类算法。
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