Agglomerative clustering starts with individual points and merges them into clusters, while divisive clustering starts with one cluster and splits it into smaller clusters.
Questions & Step-by-step Solutions
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Q
Q: In hierarchical clustering, what is the difference between agglomerative and divisive methods?
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.
Steps: 6
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.
