Cluster Analysis Quiz

What is the goal of cluster analysis?

• To organize and group similar data points

• To classify data into distinct categories

• To summarize data using statistical measures

• To predict future data points

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

• Handles large datasets efficiently

• Can detect clusters of various shapes

• Does not require specifying the number of clusters in advance

• Provides a visual representation of the clustering process

What is the main disadvantage of K-means clustering?

• Sensitivity to outliers

• Requires specifying the number of clusters in advance

• Computational complexity for big data scenarios

• Difficulty in determining the optimal number of clusters

What does hierarchical clustering produce?

• A dendrogram

• A scatter plot

• A decision tree

• A confusion matrix

Which of the following is an advantage of hierarchical clustering?

• Flexibility in determining the number of clusters

• Efficient for large datasets

• Handles outliers effectively

• Assumes spherical clusters

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

• Based on the Euclidean distance between data points

• Based on the Manhattan distance between data points

• Based on the correlation between data points

• Based on the cosine similarity between data points

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

• To ensure even distribution of data points in clusters

• To minimize the within-cluster sum of squares

• To handle outliers effectively

• To visualize the clustering process

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

• Running the algorithm multiple times with different initializations

• Increasing the number of clusters (K)

• Using a different distance metric than Euclidean distance

• Applying outlier detection techniques to remove outliers

What is one disadvantage of hierarchical clustering?

• Computational complexity for large datasets

• Insensitivity to outliers

• Lack of flexibility in determining the number of clusters

• Assumes spherical clusters

How does hierarchical clustering handle the number of clusters?

• It allows for flexible determination of the number of clusters

• It requires specifying the number of clusters in advance

• It automatically determines the optimal number of clusters

• It assumes a fixed number of clusters throughout the process