返回kmeans聚类中最远的离群值？ [英] Return the furthermost outlier in kmeans clustering?

问题描述

在sklearn kmeans聚类之后，是否有任何简单的方法可以返回最远的离群值？

Is there any easy way to return the furthermost outlier after sklearn kmeans clustering?

本质上，我想列出负载最大的离群值列表。不幸的是，由于分配，我需要使用sklearn.cluster.KMeans。

Essentially I want to make a list of the biggest outliers for a load of clusters. Unfortunately I need to use sklearn.cluster.KMeans due to the assignment.

推荐答案

K-means不适用于离群值检测。

K-means is not well suited for "outlier" detection.

k均值有将离群值变成一个单元素簇的趋势。然后离群值具有最小可能的距离，并且不会被检测到。

k-means has a tendency to make outliers a one-element cluster. Then the outliers have the smallest possible distance and will not be detected.

当数据中存在离群值时，K均值不够鲁棒。您实际上可能想在使用k均值之前删除异常值。

K-means is not robust enough when there are outliers in your data. You may actually want to remove outliers prior to using k-means.

请改用kNN，LOF或LoOP之类的东西。

Use rather something like kNN, LOF or LoOP instead.

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