k-medianoids聚类算法

James Cha, Teryn Cha, Sung-Hyuk Cha
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引用次数: 0

摘要

最简单和流行的聚类方法之一是简单k-均值聚类算法。该方法的缺点之一是对异常值很敏感。为了克服这个问题,使用了k中位数聚类算法。简单k-means聚类算法的另一个限制是欧几里德空间假设。k-介质已被用来克服这一假设。本文提出了一种称为k-中似类聚类算法的组合方法。中位线是一种不需要欧几里得空间假设的中位线,它的定义是形式化的。用核苷酸序列证明了所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
k-medianoids Clustering Algorithm
One of the simplest and popular clustering method is the simple k-means clustering algorithm. One of the drawbacks of the method is its sensitivity to outliers. To overcome this problem, the k-medians clustering algorithm is used. Another limitation of the simple k-means clustering algorithm is the Euclidean space assumption. The k-medoids has been used to overcome this assumption. Here a combined method called the k-medianoids clustering algorithm is proposed. A medianoid is a kind of median that does not require the Euclidean space assumption and is formally defined. The proposed method is demonstrated using nucleotide sequences.
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