What application areas typically use unsupervised learning methods?

Unsupervised learning methods are often used in situations where the goal is to identify patterns and groupings in data without pre-labeled outcomes. Market basket analysis is a prime example of this, as it focuses on discovering relationships between items purchased together. Through techniques such as association rule learning, businesses can analyze transaction data to uncover insights about consumer behavior, such as what items frequently occur together in a shopping cart, leading to targeted promotions and inventory management.

This method differs from supervised learning, which relies on labeled data to train models to predict a specific outcome. In the case of email filtering or spam detection, these applications typically require labeled data to distinguish between spam and legitimate emails, thus leveraging supervised learning. Similarly, predictive text applications also depend on labeled data to train models on correct word sequences based on user input. Therefore, market basket analysis stands out as a clear application area that aligns with the principles of unsupervised learning.