Introduction to Unsupervised Learning

Unsupervised learning helps uncover hidden patterns and groupings in data without relying on labeled outputs. In this lesson, we’ll explore the core concepts behind unsupervised learning, apply it to customer segmentation scenarios, and review clustering techniques like k-means to understand their practical strengths and limitations. Let’s get started.

What is unsupervised learning?

Unsupervised learning involves working with datasets that do not have labeled outcomes. Explain the concept of unsupervised learning and how it differs fundamentally from supervised learning approaches.

Sample answer

Unsupervised learning involves training a machine learning model on unlabeled data, allowing the model to find patterns and relationships without predefined labels. This contrasts with supervised learning, which relies on labeled data to map inputs to specific outputs. Supervised learning is task-oriented (e.g., predicting sales), while unsupervised learning is exploratory, uncovering hidden structures in data (e.g., grouping customers based on behavior).

Clustering is a fundamental technique within the broader context of unsupervised learning. As one of its primary applications, clustering helps discover inherent groupings or patterns in data without relying on labeled outputs. By grouping similar data points based on their shared characteristics, clustering provides valuable insights into the underlying structure of datasets. This is useful in exploratory data analysis, where the goal is to better understand the data and generate hypotheses. Within the framework of unsupervised learning, clustering serves as a means to organize complex datasets into interpretable subsets, facilitating downstream tasks such as targeted marketing, anomaly detection, or personalized recommendations. It exemplifies the core objective of unsupervised learning: uncovering hidden patterns and relationships in unlabeled data.

Other techniques fall under the umbrella of unsupervised learning, including association mining, which is also referred to as association rule mining. This technique helps identify relationships or patterns between items within extensive datasets. A common application is market basket analysis, where it reveals which ...