What process involves machine learning systems identifying and learning patterns from data?

The process that involves machine learning systems identifying and learning patterns from data is pattern recognition. This approach focuses on the ability of algorithms to recognize regularities and structures within data sets. By employing techniques such as classification and regression, machine learning systems analyze data inputs to detect patterns and make predictions based on those findings.

Pattern recognition is critical in various applications, ranging from image and speech recognition to medical diagnosis and financial analytics. The underlying principles allow systems to adaptively learn and improve over time as they are exposed to new data, making it a foundational concept in the field of artificial intelligence and machine learning.

In contrast, data transformation refers to the process of converting data from one format or structure into another to prepare it for analysis. Feature extraction involves selecting relevant variables or attributes from raw data that contribute to predictive modeling or analysis. Clustering is a specific type of pattern recognition where data points are grouped based on their similarities, but it focuses on organizing data rather than solely identifying patterns.