How to understand k-center clustering in R language clustering algorithm

In this issue, the editor will bring you about how to understand k-center clustering in R language clustering algorithm. the article is rich in content and analyzes and describes it from a professional point of view. I hope you can get something after reading this article.

1. Principle analysis:

An improved algorithm aiming at the disadvantage that the K-means algorithm is easily affected by the extreme value. The difference in principle is that when selecting the center point of each category, it does not take the sample mean point, but selects the sample with the smallest sum of the distance to the rest of the sample in the category as the center.

two。 Application in R language

K-centric clustering (K-medoids) is mainly applied to the pam function of cluster package in R language (R language built-in package).

Pam (x, "dist"), metric= "euclidean", MedoidsDiscriptionNULL, standardised FALSErect cluster.onlywritten FALSEREE do.swapped trust keep.disswriting disswriting records