Abstract

For situations in which there are unlabeled data and the objective is to uncover hidden patterns, groupings or anomalies, unsupervised ML is more suitable than supervised or reinforcement learning because it identifies structure within raw data without relying on predefined outputs. This capability enhances Six Sigma and quality management by exposing inefficiencies, detecting outliers and highlighting improvement opportunities to propel data-driven process enhancement.