How to Evaluate Unsupervised Clustering Algorithms: Metrics, Scenarios, and Insights
This article explains how to assess unsupervised clustering algorithms by describing realistic user‑watching scenarios, outlining common cluster and algorithm types, presenting five key evaluation criteria, and introducing practical metrics such as RMSSTD, R‑Square, and the improved Hubert‑Gamma statistic.
