What is the main difference between hierarchical clustering and K-Means clustering?
Practice Questions
1 question
Q1
What is the main difference between hierarchical clustering and K-Means clustering?
Hierarchical clustering requires labeled data
K-Means clustering is faster
Hierarchical clustering creates a tree structure
K-Means clustering can only form circular clusters
Hierarchical clustering creates a tree structure (dendrogram) to represent the data.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the main difference between hierarchical clustering and K-Means clustering?
Solution: Hierarchical clustering creates a tree structure (dendrogram) to represent the data.
Steps: 4
Step 1: Understand that both hierarchical clustering and K-Means clustering are methods used to group similar data points together.
Step 2: Know that hierarchical clustering builds a tree-like structure called a dendrogram, which shows how data points are related to each other.
Step 3: Learn that K-Means clustering divides data into a fixed number of groups (K) based on the average of the data points in each group.
Step 4: Remember that in hierarchical clustering, you don't need to specify the number of groups in advance, while in K-Means, you must choose K before starting.
