Machine Learning Clustering 3

Question 1

When applying K-Means to high-dimensional data, which phenomenon often causes the algorithm to perform poorly?

Accepted Answer

The curse of dimensionality making distances less meaningful

Answer

In high dimensions, distances between points converge, making it difficult for K-Means to distinguish between close and far neighbors.

Question 2

Which initialization strategy for K-Means selects initial centroids that are spread far apart to improve convergence?

Accepted Answer

K-Means++

Answer

K-Means++ probabilistically selects each subsequent centroid farther from already-chosen ones, reducing the chance of poor local optima.

Question 3

In DBSCAN, what term describes a point that does not belong to any cluster?

Accepted Answer

Noise point

Answer

Noise points (also called outliers) in DBSCAN are points that are neither core points nor reachable from any core point.

Question 4

What distinguishes soft clustering from hard clustering?

Accepted Answer

Soft clustering assigns each point a probability of belonging to each cluster

Answer

In soft clustering (e.g., GMM), each point has a fractional membership across all clusters rather than exclusive assignment to one.

Question 5

Which of the following is a valid reason to prefer hierarchical clustering over K-Means?

Accepted Answer

Hierarchical clustering does not require specifying K beforehand

Answer

Hierarchical clustering builds a full dendrogram, allowing the number of clusters to be chosen by cutting the tree at any level after computation.

Machine Learning Practice Test