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In hierarchical clustering, what is the difference between agglomerative and div

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Question: In hierarchical clustering, what is the difference between agglomerative and divisive methods?

Options:

  1. Agglomerative starts with individual points, divisive starts with one cluster
  2. Agglomerative merges clusters, divisive splits clusters
  3. Both A and B
  4. None of the above

Correct Answer: Both A and B

Solution: 

Agglomerative clustering starts with individual points and merges them into clusters, while divisive clustering starts with one cluster and splits it into smaller clusters.

In hierarchical clustering, what is the difference between agglomerative and div

Practice Questions

Q1
In hierarchical clustering, what is the difference between agglomerative and divisive methods?
  1. Agglomerative starts with individual points, divisive starts with one cluster
  2. Agglomerative merges clusters, divisive splits clusters
  3. Both A and B
  4. None of the above

Questions & Step-by-Step Solutions

In hierarchical clustering, what is the difference between agglomerative and divisive methods?
Correct Answer: Agglomerative clustering merges points into clusters, while divisive clustering splits clusters into smaller ones.
  • Step 1: Understand that hierarchical clustering is a method of grouping data points into clusters based on their similarities.
  • Step 2: Learn about agglomerative clustering, which starts with each data point as its own cluster.
  • Step 3: In agglomerative clustering, the algorithm merges the closest clusters together step by step until all points are in one cluster.
  • Step 4: Now, learn about divisive clustering, which is the opposite approach.
  • Step 5: In divisive clustering, you start with all data points in one big cluster.
  • Step 6: The algorithm then splits this big cluster into smaller clusters step by step until each point is its own cluster.
  • Agglomerative Clustering – A bottom-up approach that begins with each data point as its own cluster and merges them based on similarity.
  • Divisive Clustering – A top-down approach that starts with a single cluster containing all data points and recursively splits it into smaller clusters.
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