Unsupervised Machine Learning. K-Means & Hierarchical Clustering

Unsupervised machine learning is a branch of machine learning where models are trained on data without labelled outcomes. Unlike supervised learning, where the goal is to predict a known target, unsupervised learning focuses on discovering hidden patterns, structures, or relationships within the data. Common tasks in unsupervised learning include: Clustering (grouping similar data points) Dimensionality reduction Clustering is the process of grouping data points such that points within the same cluster are similar and points in different clusters are dissimilar. Similarity is usually measured using distance metrics like: Euclidean distance (most common) Manhattan distance Cosine similarity K-Means Clustering. K-Means is a partition-based clustering algorithm that divides data into K distinct clusters, where K is predefined. The goal is to minimize the within-cluster variance, also called inertia. How K-Means Works Choose K (number of clusters) - Example: K = 3 I