K-means clustering is a widely used unsupervised learning algorithm. However, it is sensitive to outliers and assumes that features are on the same scale, which can distort clustering results if not properly normalized.
According to the AAIA™ Study Guide, this sensitivity can impact model reliability and the meaningfulness of clusters.
"Auditors should assess whether proper data preprocessing (e.g., normalization, outlier removal) was applied in clustering models. K-means assumes Euclidean distances, making it prone to errors when features differ in scale or contain outliers." Therefore, C correctly identifies the key risk.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Clustering Algorithms and Data Risks"