Description: An unsupervised learning algorithm is a type of artificial intelligence technique that allows systems to learn patterns and structures from data without the need for predefined labels or answers. Unlike supervised learning, where a model is trained with labeled data, unsupervised learning seeks to discover intrinsic relationships in the data. This is achieved by identifying similarities and differences among inputs, allowing for the grouping of similar data or reducing the dimensionality of the data. Unsupervised learning algorithms are fundamental in various fields, as they enable tasks such as data clustering, anomaly detection, and pattern recognition without human intervention. Their ability to process large volumes of data and extract relevant information makes them valuable tools in diverse applications, from improving data quality to automating data analysis processes. In summary, these algorithms are essential for developing intelligent systems that can adapt and learn from their environment without relying on labeled information, significantly expanding their applicability across different domains.
