Unsupervised Learning: Clustering for Competitive Exams

Unsupervised Learning: Clustering - Higher Difficulty Problems

Q. What is a limitation of using K-Means for clustering?

Solution

K-Means assumes that clusters are spherical and of similar size and density, which may not be true for all datasets.

Correct Answer: B — It assumes clusters are of equal size and density

Q. Which clustering algorithm is based on the concept of density?

Solution

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters based on the density of data points.

Correct Answer: C — DBSCAN

Q. Which clustering algorithm is particularly effective for identifying clusters of varying shapes and densities?

Solution

DBSCAN is effective for identifying clusters of varying shapes and densities, as it does not assume spherical clusters.

Correct Answer: C — DBSCAN

Q. Which of the following algorithms is commonly used for hierarchical clustering?

Solution

Agglomerative clustering is a popular method for hierarchical clustering, where clusters are formed by merging smaller clusters.

Correct Answer: C — Agglomerative clustering

Q. Which of the following clustering methods can handle non-spherical clusters?

Solution

DBSCAN can handle non-spherical clusters as it groups points based on density rather than distance.

Correct Answer: C — DBSCAN

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

Solution

Hierarchical clustering can be computationally expensive and is generally limited to smaller datasets due to its complexity.

Correct Answer: A — It can only handle small datasets

Q. Which of the following metrics is NOT typically used to evaluate clustering performance?

Solution

Mean Squared Error is not typically used to evaluate clustering performance; it is more relevant for regression tasks.

Correct Answer: C — Mean Squared Error

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