Q. What is a key advantage of hierarchical clustering over K-means?
A.
It requires fewer computations
B.
It does not require the number of clusters to be specified in advance
C.
It is always more accurate
D.
It can only handle small datasets
Solution
A key advantage of hierarchical clustering is that it does not require the number of clusters to be specified in advance, allowing for more flexibility.
Correct Answer:
B
— It does not require the number of clusters to be specified in advance
Q. What is the main difference between agglomerative and divisive hierarchical clustering?
A.
Agglomerative starts with individual points, while divisive starts with one cluster
B.
Agglomerative is faster than divisive
C.
Divisive clustering is more commonly used than agglomerative
D.
There is no difference; they are the same
Solution
Agglomerative clustering begins with individual data points and merges them into clusters, while divisive clustering starts with one cluster and splits it into smaller clusters.
Correct Answer:
A
— Agglomerative starts with individual points, while divisive starts with one cluster
Q. What is the main purpose of using distance metrics in clustering algorithms?
A.
To determine the number of clusters
B.
To measure the similarity or dissimilarity between data points
C.
To visualize the clusters formed
D.
To optimize the performance of the algorithm
Solution
Distance metrics are used in clustering algorithms to measure the similarity or dissimilarity between data points, which is crucial for forming clusters.
Correct Answer:
B
— To measure the similarity or dissimilarity between data points
