Optimal Number of Clusters

A dark-themed graphic titled "Optimal K-Means Clustering" featuring a split view. On the left, an "Elbow Method" graph shows WCSS decreasing as K increases, with a red dot highlighting the elbow point at K=3. Below it, data points are scattered, representing unclustered data. On the right, "Silhouette Score" bar charts compare scores for K=2, K=3, and K=4. The K=3 chart shows higher, more balanced bars and an average score of +0.75, indicating optimal clustering. Below these charts, the same data points are shown clearly divided into three distinct, colorful clusters (purple, green, blue). The overall design uses glowing lines and a subtle circuit board background, conveying a tech-savvy and analytical feel.

Elbow Method and Silhouette Score Explained: Finding the Optimal Number of Clusters in K-Means

The Elbow Method and Silhouette Score are two powerful techniques for selecting the best number of clusters in K-Means. This guide explains WCSS, inertia, and how to evaluate cluster quality using cohesion and separation.

Aryan

Sep 25

Elbow Method and Silhouette Score Explained: Finding the Optimal Number of Clusters in K-Means

© 2025 Aryan Upadhyay |