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+---
+id: K-Medians
+aliases:
+  - *K-Medians*: Handling Outliers by Computing Medians [(Youtube)]()
+tags: []
+---
+
+## _K-Medians_: Handling Outliers by Computing Medians [(Youtube)]()
+
+- Medians are less sensitive to outliers than means
+  - Think of the median salary vs. mean salary of a large firm when adding a few
+    top executives!
+- _**K-Medians**_: Instead of taking the **mean** value of the object in a
+  cluster as a reference point, **medians** are used ($L_1$-norm is often used
+  as the distance measure)
+- The criterion function for the _K-Medians_ algorithm: $$ S =
+  \sum*{k=1}^{K}\sum*{x*{i\in{C_k}}}|x*{ij} - m e d\_{kj}|$$
+- The _K-Medians_ clustering algorithm:
+  - Select _K_ points as the initial representative objects (i.e., as initial _K
+    medians_)
+  - **Repeat**
+    - Assign every point to its nearest median
+    - Re-compute the median using the median of <u>==each individual
+      feature==</u>
+  - **Until** convergence criterion is satisfied