Cluster Analysis Quiz

What is the goal of cluster analysis?

• a) To organize and group similar data points

• b) To classify data into distinct categories

• c) To summarize data using statistical measures

• d) To predict future data points

Which of the following is an advantage of K-means clustering?

• a) Handles large datasets efficiently

• b) Can detect clusters of various shapes

• c) Does not require specifying the number of clusters in advance

• d) Provides a visual representation of the clustering process

What is the main disadvantage of K-means clustering?

• a) Sensitivity to outliers

• b) Requires specifying the number of clusters in advance

• c) Computational complexity for big data scenarios

• d) Difficulty in determining the optimal number of clusters

What does hierarchical clustering produce?

• a) A dendrogram

• b) A scatter plot

• c) A decision tree

• d) A confusion matrix

Which of the following is an advantage of hierarchical clustering?

• a) Flexibility in determining the number of clusters

• b) Efficient for large datasets

• c) Handles outliers effectively

• d) Assumes spherical clusters

How are data points assigned to clusters in K-means clustering?

• a) Based on the Euclidean distance between data points

• b) Based on the Manhattan distance between data points

• c) Based on the correlation between data points

• d) Based on the cosine similarity between data points

What is the purpose of recalculating centroids in K-means clustering?

• a) To ensure even distribution of data points in clusters

• b) To minimize the within-cluster sum of squares

• c) To handle outliers effectively

• d) To visualize the clustering process

What is the recommended approach to mitigate the sensitivity of K-means clustering to initial centroid placement?

• a) Running the algorithm multiple times with different initializations

• b) Increasing the number of clusters (K)

• c) Using a different distance metric than Euclidean distance

• d) Applying outlier detection techniques to remove outliers

What is one disadvantage of hierarchical clustering?

• a) Computational complexity for large datasets

• b) Insensitivity to outliers

• c) Lack of flexibility in determining the number of clusters

• d) Assumes spherical clusters

How does hierarchical clustering handle the number of clusters?

• a) It allows for flexible determination of the number of clusters

• b) It requires specifying the number of clusters in advance

• c) It automatically determines the optimal number of clusters

• d) It assumes a fixed number of clusters throughout the process