Question: What is the main difference between K-means and hierarchical clustering?

Options:

1. K-means is a partitional method, while hierarchical is a divisive method
2. K-means requires the number of clusters to be defined, while hierarchical does not
3. K-means can only be used for numerical data, while hierarchical can handle categorical data
4. K-means is faster than hierarchical clustering for small datasets

Correct Answer: K-means requires the number of clusters to be defined, while hierarchical does not

Solution:

K-means is a partitional clustering method that divides data into a fixed number of clusters, while hierarchical clustering builds a tree of clusters without needing to specify the number of clusters in advance.