# Evaluation Metrics for Unsupervised Learning Algorithms

arXiv: https://arxiv.org/abs/1905.05667 · HTML: https://mlsanity.com/paper/1905.05667/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 16 of 16)

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- [A comprehensive study of clustering a class of 2D shapes](https://mlsanity.com/paper/2111.06662/) (2021)
- [Generating Multidimensional Clusters With Support Lines](https://mlsanity.com/paper/2301.10327/) (2023)
- [Layout-Aware OCR for Black Digital Archives with Unsupervised Evaluation](https://mlsanity.com/paper/2509.13236/) (2025)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [Mixed Data Clustering Survey and Challenges](https://mlsanity.com/paper/2512.03070/) (2025)
- [Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook](https://mlsanity.com/paper/2112.04889/) (2021)
- [Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies](https://mlsanity.com/paper/2203.04107/) (2022)
- [Towards Unsupervised Representation Learning: Learning, Evaluating and Transferring Visual Representations](https://mlsanity.com/paper/2312.00101/) (2023)

## Abstract (arXiv)

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.