Machine Learning — Cluster Validation: The Elbow Method and Silhouette Score
Cluster analysis is a fundamental technique in unsupervised machine learning that involves grouping similar data points into clusters or groups. It plays a crucial role in various applications, from customer segmentation to image compression. Two common methods for determining the optimal number of clusters in a dataset are the “Elbow Method” and the “Silhouette Score.”
ELBOW METHOD
The Elbow Method is a simple yet effective technique for finding the optimal number of clusters (k) in a dataset.
It relies on the intuition that as you increase the number of clusters, the within-cluster variation (also known as the Sum of Squared Distances or SSD) or WCSS (Within cluster sum squared) typically decreases. However, there is a point where adding more clusters does not significantly reduce the SSD.
This point is known as the “elbow point,” and it represents a trade-off between minimizing the WCSS and avoiding overfitting.
The steps to apply the Elbow Method are as follows:
- Run the k-means clustering algorithm for a range…
