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---
id: Compare and Contrast
aliases:
- clustering algorithms
tags:
- Compare-and-Contrast
---
## [[clustering algorithms]]
- [[K-Means]] vs [[K-Medoids]]
- In _K-means_ algorithm, they choose means as the centroids but in the
_K-medoids_, data points are chosen to be the medoids[^1].
- [[K-Means]] vs [[K-Medians]]
| K-Means | K-Medians |
| ---------------------------------------------------------- | --------------------------------------------- |
| The center is not necessarily one of the input data points | Centers will be chosen from data points |
| Not flexible | More flexible |
| Not immune to noise and outliers | More robust to noise and outliers |
| Minimize the sum of squared Euclidian distance | Minimize a sum of pairwise of dissimilarities |
[^1]:
Medoids are **representative objects of a data set or a cluster within a
data set whose sum of dissimilarities to all the objects in the cluster is
minimal**. Medoids are similar in concept to means or centroids, but medoids are
always restricted to be members of the data set.
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