As outlined in the AI-900 study guide and Microsoft Learn's "Explore fundamental principles of machine learning" module, clustering is a core example of unsupervised machine learning.
In unsupervised learning, the model is trained on data without labeled outcomes. The goal is to discover patterns or groupings naturally present in the data. Clustering algorithms, such as K-means, DBSCAN, or Hierarchical clustering, analyze similarities among data points and group them into clusters. For example, clustering can group customers by purchasing behavior or segment products by shared characteristics - all without predefined labels.
Supervised learning, by contrast, uses labeled data (input-output pairs) to train a model that predicts outcomes. This includes:
* A. Multiclass classification - Predicts more than two categories (e.g., classifying images as dog, cat, or bird).
* C. Binary classification - Predicts two categories (e.g., spam vs. not spam).
* D. Regression - Predicts continuous numeric values (e.g., price prediction).
Therefore, the only option representing unsupervised learning is clustering, which enables data discovery without predefined labels.