Clustering

Introduction Clustering is an essential unsupervised learning technique that aims to group similar data points together based on their similarity. It finds applications in various domains, such as customer segmentation, anomaly detection, and image segmentation.

Introduction In the world of unsupervised learning, clustering is a powerful technique that allows us to discover patterns and structure within datasets without any labeled outcomes. In our previous blog posts, we explored the widely used K-Means and DBSCAN clustering algorithms with the famous Iris dataset.

Introduction In this post, we will explore two essential techniques in the field of statistics and data analysis: clustering and principal component analysis (PCA). Clustering is a method used to group similar data points together based on their similarity, while PCA is a dimensionality reduction technique that helps us understand the underlying structure of our data.