Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm is given data without explicit instructions on what to do with it. The system must find patterns, structures, or groupings within the input data on its own.

In finance, this is used for clustering assets with similar performance characteristics or identifying hidden relationships in transaction data. It is powerful because it can discover insights that human analysts might miss.

Because it does not require labeled training data, it is highly scalable for massive datasets. It is the foundation for techniques like topic modeling and anomaly detection in blockchain networks.

It enables a data-driven approach to market analysis without the bias of pre-existing assumptions.