Machine Learning Hero

5.1 Clustering (K-Means, Hierarchical, DBSCAN)

Clustering is a fundamental and widely-used technique in unsupervised learning. At its core, clustering aims to partition a dataset into distinct groups, or clusters, based on inherent similarities among data points. The key principle is that data points within the same cluster should exhibit a higher degree of similarity to each other compared to points in other clusters. This similarity is typically measured using distance metrics such as Euclidean distance, Manhattan distance, or cosine similarity, depending on the nature of the data and the specific clustering algorithm employed.

The power of clustering lies in its ability to uncover hidden patterns and structures within complex, high-dimensional ...