Clustering Methods: K-means, Hierarchical for Exam Prep

Clustering Methods: K-means, Hierarchical - Case Studies

Q. In which scenario would you prefer hierarchical clustering over K-means?

A. When the number of clusters is known
B. When the dataset is very large
C. When you need a visual representation of the clustering process
D. When clusters are expected to be spherical

Solution

Hierarchical clustering is preferred when a visual representation of the clustering process is needed, as it provides a dendrogram.

Correct Answer: C — When you need a visual representation of the clustering process

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Q. What is the main advantage of using K-means clustering?

A. It can find non-linear relationships
B. It is easy to implement and computationally efficient
C. It does not require any assumptions about the data distribution
D. It can handle large datasets without any limitations

Solution

The main advantage of K-means clustering is its simplicity and computational efficiency, making it easy to implement for large datasets.

Correct Answer: B — It is easy to implement and computationally efficient

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Q. What is the primary method used to determine the optimal number of clusters in K-means?

A. Elbow method
B. Silhouette analysis
C. Cross-validation
D. Grid search

Solution

The elbow method is a common technique used to determine the optimal number of clusters in K-means by plotting the explained variance against the number of clusters.

Correct Answer: A — Elbow method

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Q. What type of data is hierarchical clustering particularly useful for?

A. Large datasets with millions of records
B. Data with a clear number of clusters
C. Data where relationships between clusters are important
D. Data that is strictly numerical

Solution

Hierarchical clustering is particularly useful for data where understanding the relationships between clusters is important, as it provides a visual representation of these relationships.

Correct Answer: C — Data where relationships between clusters are important

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Q. Which of the following statements is true regarding K-means clustering?

A. It can only be applied to spherical clusters
B. It is sensitive to the initial placement of centroids
C. It guarantees finding the global optimum
D. It can handle categorical data directly

Solution

K-means clustering is sensitive to the initial placement of centroids, which can affect the final clustering results.

Correct Answer: B — It is sensitive to the initial placement of centroids

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Clustering Methods: K-means, Hierarchical - Case Studies

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