Which clustering method is more suitable for discovering non-globular shapes in

Practice Questions

Q1
Which clustering method is more suitable for discovering non-globular shapes in data?
  1. K-means clustering
  2. Hierarchical clustering
  3. DBSCAN
  4. Gaussian Mixture Models

Questions & Step-by-Step Solutions

Which clustering method is more suitable for discovering non-globular shapes in data?
Correct Answer: DBSCAN
  • Step 1: Understand what clustering means. Clustering is a way to group similar data points together.
  • Step 2: Learn about different clustering methods. Some methods work better for certain shapes of data.
  • Step 3: Identify the types of shapes in data. Globular shapes are round, while non-globular shapes can be irregular or elongated.
  • Step 4: Discover DBSCAN. DBSCAN is a clustering method that can find clusters of different shapes and sizes.
  • Step 5: Compare DBSCAN with other methods. Unlike methods that assume round shapes, DBSCAN can handle non-globular shapes well.
  • Step 6: Conclude that DBSCAN is suitable for non-globular data because it can identify clusters that are not round.
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