In which scenario would hierarchical clustering be preferred over K-means?
Practice Questions
1 question
Q1
In which scenario would hierarchical clustering be preferred over K-means?
When the number of clusters is known
When the dataset is very large
When a hierarchy of clusters is desired
When the data is strictly numerical
Hierarchical clustering is preferred when a hierarchy of clusters is desired, as it provides a tree-like structure of the data.
Questions & Step-by-step Solutions
1 item
Q
Q: In which scenario would hierarchical clustering be preferred over K-means?
Solution: Hierarchical clustering is preferred when a hierarchy of clusters is desired, as it provides a tree-like structure of the data.
Steps: 5
Step 1: Understand what hierarchical clustering is. It groups data into a tree-like structure called a dendrogram.
Step 2: Know what K-means clustering is. It divides data into a fixed number of clusters (K) based on their similarities.
Step 3: Identify when you need a hierarchy. If you want to see how clusters are related or nested within each other, use hierarchical clustering.
Step 4: Consider the size of your data. Hierarchical clustering can be slow with large datasets, while K-means is faster.
Step 5: Think about the number of clusters. If you don't know how many clusters you want, hierarchical clustering can help you decide by showing the tree structure.
