labrijisaad/optimal-k-in-k-means-clustering

Using the Elbow Method and Silhouette Analysis to find the optimal K in K-Means Clustering.

Optimal K in K-Means Clustering 📊

Introduction 🌟

This notebook is dedicated to exploring the optimal number of clusters (K) in K-Means clustering, an important step in unsupervised learning. It uses two renowned methods: the Elbow Method and Silhouette Analysis, providing insights into their mathematical formulas and practical applications.

Importance of Selecting the Right Number of Clusters 🔑

The choice of K is pivotal in clustering:

• An underestimated K may lead to the merging of distinct groups, obscuring valuable insights 🌐.
• An overestimated K could result in overfitting, capturing noise rather than the actual patterns, potentially forming meaningless clusters 🚫.

Theoretical Background 📚

• Elbow Method: This method involves plotting the Within-Cluster Sum of Squares (WCSS) and identifying the 'elbow' point where the rate of decrease sharply changes. This point suggests a suitable number of clusters 📉.
• Silhouette Analysis: This technique evaluates how similar a data point is to its own cluster compared to others. It calculates a silhouette score for each point, aiding in assessing the separation distance between the resulting clusters 📏.

Example and Visualization 📈

An example is provided in the notebook, illustrating the practical application of these methods on a dataset. It includes generating mock data (where we already know the value of K), applying both Elbow and Silhouette analyses, and interpreting the results to determine the K again.

Connect 🌐