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What is a key advantage of using hierarchical clustering over K-means?
What is a key advantage of using hierarchical clustering over K-means?
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What is a key advantage of using hierarchical clustering over K-means?
It requires less computational power
It does not require the number of clusters to be specified in advance
It is always more accurate
It can handle larger datasets
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Hierarchical clustering does not require the number of clusters to be predetermined, allowing for more flexibility in exploring data.
Questions & Step-by-step Solutions
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Q
Q: What is a key advantage of using hierarchical clustering over K-means?
Solution:
Hierarchical clustering does not require the number of clusters to be predetermined, allowing for more flexibility in exploring data.
Steps: 5
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Step 1: Understand that clustering is a way to group similar data points together.
Step 2: Know that K-means clustering requires you to decide how many groups (clusters) you want before starting.
Step 3: Realize that hierarchical clustering does not need you to set the number of clusters in advance.
Step 4: Recognize that this flexibility allows you to explore the data and find the best number of clusters based on the data itself.
Step 5: Conclude that the key advantage of hierarchical clustering is its ability to adapt to the data without needing a fixed number of clusters.
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