What is the main advantage of using hierarchical clustering over K-means?
Practice Questions
1 question
Q1
What is the main advantage of using hierarchical clustering over K-means?
It is faster and more efficient
It does not require the number of clusters to be specified
It can handle large datasets better
It is less sensitive to outliers
Hierarchical clustering does not require the number of clusters to be specified in advance, allowing for more flexibility in cluster formation.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the main advantage of using hierarchical clustering over K-means?
Solution: Hierarchical clustering does not require the number of clusters to be specified in advance, allowing for more flexibility in cluster formation.
Steps: 5
Step 1: Understand that clustering is a way to group similar items together.
Step 2: Know that K-means clustering requires you to decide how many groups (clusters) you want before starting.
Step 3: Realize that hierarchical clustering does not need you to decide the number of groups beforehand.
Step 4: Recognize that this flexibility in hierarchical clustering allows you to explore different groupings of the data.
Step 5: Conclude that the main advantage of hierarchical clustering is its ability to form clusters without needing a pre-set number.
