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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.