What is the main advantage of using Gaussian Mixture Models (GMM) for clustering

Practice Questions

Q1
What is the main advantage of using Gaussian Mixture Models (GMM) for clustering?
  1. It is faster than K-Means
  2. It can model clusters with different shapes and sizes
  3. It requires no prior knowledge of the number of clusters
  4. It is less sensitive to outliers

Questions & Step-by-Step Solutions

What is the main advantage of using Gaussian Mixture Models (GMM) for clustering?
  • Step 1: Understand that clustering is a way to group similar data points together.
  • Step 2: Know that traditional clustering methods, like K-means, assume that all clusters are round and of similar size.
  • Step 3: Learn that Gaussian Mixture Models (GMM) can represent clusters that are not just round but can have different shapes and sizes.
  • Step 4: Realize that GMM uses a combination of multiple Gaussian distributions to fit the data, allowing for more flexibility in modeling clusters.
  • Step 5: Conclude that the main advantage of GMM is its ability to accurately model complex data distributions.
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