My Learning Cart
?
Categories
Account

What is the main limitation of K-Means clustering?

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

What’s inside this PDF?

Question: What is the main limitation of K-Means clustering?

Options:

  1. It is computationally expensive
  2. It requires a predefined number of clusters
  3. It can only handle numerical data
  4. It is sensitive to outliers

Correct Answer: It requires a predefined number of clusters

Solution: 

K-Means requires the user to specify the number of clusters in advance, which can be a limitation.

What is the main limitation of K-Means clustering?

Practice Questions

Q1
What is the main limitation of K-Means clustering?
  1. It is computationally expensive
  2. It requires a predefined number of clusters
  3. It can only handle numerical data
  4. It is sensitive to outliers

Questions & Step-by-Step Solutions

What is the main limitation of K-Means clustering?
  • Step 1: Understand that K-Means is a method used to group data into clusters.
  • Step 2: Know that a cluster is a collection of similar data points.
  • Step 3: Realize that before using K-Means, you must decide how many clusters you want to create.
  • Step 4: Recognize that this requirement can be a limitation because you may not know the best number of clusters in advance.
  • Step 5: Understand that choosing the wrong number of clusters can lead to poor results.
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