My Learning Cart
?
Categories
Account

What is a potential drawback 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 a potential drawback of K-Means clustering?

Options:

  1. It can handle non-linear data well
  2. It requires the number of clusters to be specified in advance
  3. It is computationally inexpensive
  4. It is robust to outliers

Correct Answer: It requires the number of clusters to be specified in advance

Solution: 

K-Means requires the number of clusters to be specified beforehand, which can be a limitation.

What is a potential drawback of K-Means clustering?

Practice Questions

Q1
What is a potential drawback of K-Means clustering?
  1. It can handle non-linear data well
  2. It requires the number of clusters to be specified in advance
  3. It is computationally inexpensive
  4. It is robust to outliers

Questions & Step-by-Step Solutions

What is a potential drawback of K-Means clustering?
  • Step 1: Understand that K-Means is a method used to group data into clusters.
  • Step 2: Know that when using K-Means, you have to decide how many clusters you want before starting.
  • Step 3: Realize that if you choose the wrong number of clusters, the results may not be accurate or useful.
  • Step 4: Recognize that this requirement to specify the number of clusters can be a limitation of K-Means.
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