Which type of data often necessitates the use of unsupervised learning techniques?

Unsupervised learning techniques are particularly suited for unstructured data because this type of data does not have labeled outputs. Unstructured data includes formats such as text, images, audio, and video, which do not follow a predefined structure, making it challenging to apply traditional supervised learning methods.

In the context of machine learning, unsupervised learning is about finding hidden patterns or intrinsic structures in input data without any prior labels. For instance, clustering algorithms can group similar text documents, images, or customer reviews without needing labeled categories. This capability is essential when dealing with vast amounts of unstructured data typically found in real-world applications where the patterns are not evident, and labeling is impractical or impossible.

The other types of data mentioned (structured, numerical, and transactional) typically work better with supervised learning techniques, which rely on labeled datasets where outputs are known.