#clustering-evaluation

Question 1: Select all that apply

mediumAI-checkedClustering

Which statements about hierarchical clustering and evaluating clusterings are true? Select all that apply.

In hierarchical clustering we do not have to worry about initialization1
Hierarchical clustering can only be applied to small datasets, because dendrograms are hard to visualize for large ones2
K-Means, DBSCAN and hierarchical clustering each have a setting that controls how coarse or fine the clustering is3
To get a robust clustering, you can combine the labels of several clustering methods by majority vote4
A high silhouette score with clean, stable clusters means the clusters capture the meaning you are interested in5