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What is a potential drawback of hierarchical clustering?
Practice Questions
Q1
What is a potential drawback of hierarchical clustering?
It can handle large datasets efficiently
It does not require a predefined number of clusters
It can be computationally expensive for large datasets
It is less interpretable than K-means
Questions & Step-by-Step Solutions
What is a potential drawback of hierarchical clustering?
Steps
Concepts
Step 1: Understand what hierarchical clustering is. It is a method of grouping data points into a tree-like structure based on their similarities.
Step 2: Recognize that hierarchical clustering involves calculating distances between all data points.
Step 3: Realize that as the number of data points increases, the number of distance calculations also increases significantly.
Step 4: Acknowledge that this increase in calculations can make the process slow and require a lot of computer resources.
Step 5: Conclude that for large datasets, this can be a major drawback, making hierarchical clustering less practical.
No concepts available.
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