Evaluation Metrics for Unsupervised Learning Algorithms
This work provides a review of evaluation metrics for clustering, which is incremental as it synthesizes existing studies without introducing new methods.
The paper addresses the challenge of evaluating clustering results in unsupervised learning, noting that Kleinberg's impossibility theorem necessitates diverse evaluation techniques tailored to specific clustering problems and algorithms.
Determining the quality of the results obtained by clustering techniques is a key issue in unsupervised machine learning. Many authors have discussed the desirable features of good clustering algorithms. However, Jon Kleinberg established an impossibility theorem for clustering. As a consequence, a wealth of studies have proposed techniques to evaluate the quality of clustering results depending on the characteristics of the clustering problem and the algorithmic technique employed to cluster data.