Evaluation Metrics for Unsupervised Learning Algorithms

TL;DR

This paper discusses the challenges and methods for evaluating the quality of clustering results in unsupervised learning, considering the theoretical limitations and various evaluation techniques.

Contribution

It reviews existing evaluation metrics for clustering, highlighting the impact of Kleinberg's impossibility theorem on the development of these metrics.

Findings

Analysis of the limitations imposed by Kleinberg's theorem

Overview of different clustering evaluation techniques

Discussion on the suitability of metrics for various clustering scenarios

Abstract

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.