MLS-C01 Question 119
Select 3You are tasked with performing a cluster analysis on customer transaction data to identify distinct groups of customers for targeted marketing. After pre-processing the data, you decide to use the k-means clustering algorithm. How can you determine the optimal number of clusters to use in your analysis?
- A
Use the elbow plot method to analyze the within-cluster sum of squares for different values of k.
- B
Use hierarchical clustering to visualize the dendrogram and identify natural splits in the data.
- C
Choose the number of clusters that maximizes the silhouette score.
- D
Select an arbitrary number of clusters based on business intuition.
- E
Use a pre-trained machine learning model to determine the optimal number of clusters.
Show answer and explanation
Correct answers: A, B, C
Explanation
Determining the optimal number of clusters is a critical step in cluster analysis. Techniques such as the elbow plot, hierarchical clustering with dendrograms, and maximizing the silhouette score are widely used methods to identify the most suitable number of clusters based on the data structure. Arbitrary selection or relying on unrelated pre-trained models is not appropriate for this task.
- A. Correct.
Correct. The elbow plot method is commonly used to determine the optimal number of clusters. By plotting the within-cluster sum of squares against the number of clusters (k), you can identify the 'elbow point' where adding more clusters provides diminishing returns.
- B. Correct.
Correct. Hierarchical clustering and dendrograms can help visualize the data structure, revealing natural groupings and splits in the data that assist in determining the number of clusters.
- C. Correct.
Correct. The silhouette score measures how well each data point is clustered and how similar it is to its own cluster compared to other clusters. Maximizing the silhouette score can help determine the optimal number of clusters.
- D. Incorrect.
Incorrect. While business intuition can guide analysis, selecting an arbitrary number of clusters without validating using statistical or visualization methods may lead to suboptimal results.
- E. Incorrect.
Incorrect. Pre-trained machine learning models are not directly used to determine the optimal number of clusters. Clustering analysis typically involves exploratory techniques rather than pre-trained models.