My Learning Cart
?
Categories
Account

What is the main difference between hierarchical clustering and K-Means clusteri

₹0.0
Login to Download
  • 📥 Instant PDF Download
  • ♾ Lifetime Access
  • 🛡 Secure & Original Content

What’s inside this PDF?

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

Options:

  1. Hierarchical clustering requires labeled data
  2. K-Means clustering is faster
  3. Hierarchical clustering creates a tree structure
  4. K-Means clustering can only form circular clusters

Correct Answer: Hierarchical clustering creates a tree structure

Solution: 

Hierarchical clustering creates a tree structure (dendrogram) to represent the data.

What is the main difference between hierarchical clustering and K-Means clusteri

Practice Questions

Q1
What is the main difference between hierarchical clustering and K-Means clustering?
  1. Hierarchical clustering requires labeled data
  2. K-Means clustering is faster
  3. Hierarchical clustering creates a tree structure
  4. K-Means clustering can only form circular clusters

Questions & Step-by-Step Solutions

What is the main difference between hierarchical clustering and K-Means clustering?
  • 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.
No concepts available.
Soulshift Feedback ×

On a scale of 0–10, how likely are you to recommend The Soulshift Academy?

Not likely Very likely
Home Practice Performance eBooks