K-Means Clustering Quiz

What is the primary goal of partitioning clustering?

• Maximize the similarity between different clusters

• Minimize the similarity within each cluster

• Maximize the similarity within each cluster

• Minimize the number of clusters

Which of the following is a characteristic of K-means clustering?

• It can handle overlapping clusters

• It requires the number of clusters to be known beforehand

• It uses hierarchical methods for clustering

• It is not sensitive to initial cluster centers

What does K-medoids clustering use as cluster centers?

• Actual data points (medoids)

• Centroids calculated from the data

• Mean of the data points

• Randomly selected points

Which method is used to determine the optimal number of clusters in K-means?

• Random sampling

• Silhouette analysis

• Principal component analysis

• Hierarchical clustering

What is a disadvantage of K-means clustering?

• It can handle any shape of clusters

• It is sensitive to outliers

• It requires a large amount of data

• It is computationally intensive

In K-means clustering, what is the first step?

• Assign points to the nearest centroid

• Calculate the distance metric

• Choose the number of clusters (k)

• Update centroids

What is the purpose of the Elbow Method?

• To visualize the clusters formed

• To determine the optimal number of clusters

• To calculate the distance between points

• To initialize the centroids

Which of the following is NOT a partitioning clustering method?

• K-means clustering

• K-medoids clustering

• CLARA clustering

• Hierarchical clustering

What does the silhouette coefficient measure?

• The distance between clusters

• The compactness of clusters

• The quality of clustering

• The separation between clusters

What is a key advantage of K-means clustering?

• It can handle non-spherical clusters

• It is always guaranteed to find the global optimum

• It requires no prior knowledge of data

• It is easy to interpret results