MLS-C01 Question 120
Select 3You are working as a data scientist for a healthcare company and need to group patients into clusters based on their medical history and lab test results. You want to determine the optimal number of clusters before applying a clustering algorithm. Which of the following steps should you take to achieve this?
- A
Use the elbow plot to evaluate the sum of squared distances (SSD) for different cluster counts.
- B
Perform hierarchical clustering and analyze the resulting dendrogram to identify natural groups.
- C
Use the silhouette score to compare different numbers of clusters and select the one with the highest average score.
- D
Apply k-means clustering with a fixed number of clusters and evaluate individual cluster sizes.
- E
Directly visualize the data in a scatter plot to determine the 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 silhouette scores are well-established methods for this purpose. These methods provide quantitative and visual insights, enabling you to make informed decisions about the number of clusters. Simply analyzing cluster sizes or visualizing data might provide some insights but are not sufficient for determining the cluster count in a systematic way.
- A. Correct.
Using the elbow plot is a common method to determine the optimal number of clusters by analyzing the SSD for different cluster counts. The 'elbow point' typically indicates the most suitable number of clusters.
- B. Correct.
Hierarchical clustering produces a dendrogram, which is a visual representation of how the data can be grouped. By inspecting this dendrogram, you can identify natural clusters and decide on the optimal number.
- C. Correct.
The silhouette score helps evaluate the quality of clustering. A higher average silhouette score indicates better-defined clusters, making it a useful metric for selecting the optimal number of clusters.
- D. Incorrect.
While analyzing cluster sizes can provide insights into balance and distribution, it does not help determine the optimal number of clusters beforehand.
- E. Incorrect.
Visualizing data in a scatter plot is useful for understanding overall patterns, but for high-dimensional data or complex distributions, it is not a reliable method for determining the number of clusters.