In which scenario would K-means clustering be preferred over hierarchical cluste

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Question: In which scenario would K-means clustering be preferred over hierarchical clustering?

Options:

When the number of clusters is unknown
When computational efficiency is a priority
When the data is not well-separated
When a detailed cluster hierarchy is needed

Correct Answer: When computational efficiency is a priority

Solution:

K-means clustering is preferred when computational efficiency is a priority, especially for large datasets, as it is generally faster than hierarchical clustering.

In which scenario would K-means clustering be preferred over hierarchical cluste

Practice Questions

In which scenario would K-means clustering be preferred over hierarchical clustering?

When the number of clusters is unknown
When computational efficiency is a priority
When the data is not well-separated
When a detailed cluster hierarchy is needed

Questions & Step-by-Step Solutions

In which scenario would K-means clustering be preferred over hierarchical clustering?

K-means Clustering – A method of clustering that partitions data into K distinct clusters based on feature similarity, optimizing for computational efficiency.
Hierarchical Clustering – A clustering method that builds a hierarchy of clusters either through agglomerative (bottom-up) or divisive (top-down) approaches.
Computational Efficiency – The speed and resource usage of an algorithm, which is crucial when dealing with large datasets.

In which scenario would K-means clustering be preferred over hierarchical cluste

What’s inside this PDF?

In which scenario would K-means clustering be preferred over hierarchical cluste

Practice Questions

Questions & Step-by-Step Solutions

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