The possible number of clusters is 2, 3, 4, or 5. We compute the corresponding number of regions per cluster:

Sun, 03 May 2026 07:50:43 +0000

Understanding the Possible Number of Clusters: When the Count Could Be 2, 3, 4, or 5 In the field of unsupervised machine learning, particularly clustering algorithms like K-means, hierarchical clustering, and Gaussian Mixture Models (GMM), one fundamental question arises: How many clusters should we identify? While clustering methods commonly allow flexibility—such as selecting 2, 3, 4, or even 5 clusters—the underlying structure of the data often constrains this choice. Among the most frequently considered groupings are 2, 3, 4, or 5 clusters, each offering unique insights depending on the dataset's inherent patterns. In this article, we explore why 2, 3, 4, or 5 clusters might be the appropriate number to consider—and how the number of resulting regions increases with each cluster addition. By analyzing the combinatorial growth of regions per cluster, we uncover the mathematical and practical significance behind these common cluster counts. ---