Clustering Methods: K-means, Hierarchical for Exam Prep

Clustering Methods: K-means, Hierarchical

Q. What is a common application of clustering methods in real-world scenarios?

Solution

Clustering methods are commonly used to segment customers based on purchasing behavior, allowing businesses to tailor marketing strategies.

Correct Answer: B — Segmenting customers based on purchasing behavior

Q. What is a potential drawback of hierarchical clustering?

Solution

Hierarchical clustering can be computationally expensive, especially for large datasets, due to its complexity.

Correct Answer: C — It can be computationally expensive for large datasets

Q. Which clustering method is more sensitive to outliers?

Solution

K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.

Correct Answer: A — K-means clustering

Q. Which clustering method is more suitable for discovering non-spherical clusters?

Solution

Hierarchical clustering can be more suitable for discovering non-spherical clusters as it does not assume a specific shape for the clusters.

Correct Answer: B — Hierarchical clustering

Q. Which of the following is a characteristic of hierarchical clustering?

Solution

Hierarchical clustering can produce a dendrogram, which is a tree-like diagram that shows the arrangement of the clusters.

Correct Answer: B — It can produce a dendrogram to visualize the clustering process

Q. Which of the following is NOT a common initialization method for K-means?

Solution

Hierarchical initialization is not a common method for initializing K-means; the other three are standard techniques.

Correct Answer: D — Hierarchical initialization

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