K-Medoids/K-Means算法.两个或多个集群代表之间具有相等距离的数据点 [英] K-Medoids / K-Means Algorithm. Data point with the equal distances between two or more cluster representatives

问题描述

我一直在研究和研究基于分区的聚类算法，例如K-means和K-Medoids.我了解到，与K-means相比，K-medoids对异常值的鲁棒性更高.但是，我对如果在分配数据点期间两个或多个集群代表在数据点上具有相同的距离会发生什么情况感到好奇.您将为哪个群集分配数据点?将数据指向一个群集的分配会极大地影响群集结果吗?

I have been researching and studying about partition-based clustering algorithms like K-means and K-Medoids. I have learned that K-medoids is more robust to outliers compared to K-means. However I am curious on what will happen if during the assigning of data points, two or more cluster representatives have the same distance on a data point. Which cluster will you assign the data point? Will the assignment of the data point to a cluster greatly affect the clustering results?

推荐答案

为防止不良情况的发生(无限循环等)，总是更喜欢束缚时已分配该点的簇.

To prevent bad things from happening (infinite loops etc.) always prefer the cluster the point already is assigned to when tied.

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