Group1: Clustering for customer segmentation and application in bank analysis

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1.How is the optimal number of clusters typically determined in K-means clustering?

• By selecting the number of clusters that minimizes the within-cluster variance
• By using domain knowledge or expert judgment
• By employing an elbow plot or silhouette analysis
• By using cross-validation

2. Which of the following is a limitation of K-means clustering?

• Sensitivity to the initial placement of cluster centroids
• Inability to handle missing data
• Inability to handle categorical data
• All of the above

3. In K-means clustering, what is the purpose of the "elbow method"

• To determine the optimal number of clusters
• To identify the best distance metric
• To select the best initialization method
• To determine the convergence criteria

4. What type of algorithm does the K-means clustering method belong to?

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Deep learning

5. The silhouette coefficient in the K-means clustering method is used to:

• Measure the similarity between data points in the same group
• Measure the similarity between data points belonging to different groups
• Evaluate the overall clustering quality
• Quantify the variation of data points after each cluster center update

6. What is the K-means clustering method used in the banking industry for?

• Predict the value of customers' assets
• Determine the customer's credit risk level
• Segment customers based on their behavior and characteristics
• Identify fraudulent transactions

7. The distance d between the points A (3, 7) and B (3, 4)

• 3
• 4
• 5
• 6

8. What is the correct formula to calculate the silhouette coefficient?

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