---
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.