Update README.md

README.md

@@ -16,7 +16,8 @@ In MtL, meta-features are designed to extract general properties able to charact
 * **Statistical**: Standard statistical measures to describe the numerical properties of a distribution of data.
 * **Information-theoretic**: Particularly appropriate to describe discrete (categorical) attributes and their relationship with the classes.
 * **Model-based**: Measures designed to extract characteristics like the depth, the shape and size of a Decision Tree (DT) model induced from a dataset.
-* **Landmarking**: Represents the performance of simple and efficient learning algorithms. Include the subsampling and relative strategies to decrease the computation cost and enrich the relations between these meta-features.
+* **Landmarking**: Represents the performance of simple and efficient learning algorithms. Include the subsampling and relative strategies to decrease the computation cost and enrich the relations between these meta-features. (relative and subsampling landmarking are also available)
+* **Clustering:** Clustering measures extract information about dataset based on external validation indexes.
 
 ## Dependencies