mlsanityData to 7 Oct 2026

Evaluation Metrics for Unsupervised Learning Algorithms

arXiv 1905.05667 ↗First posted 14 May 2019cs.LG stat.MLcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2019.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 4; 2024: 1; 2025: 4.

16 papers.

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  1. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  2. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  3. 2021A Survey on Data-driven Software Vulnerability Assessment and Prioritizationcited by 18
  4. 2024MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Modelscited by 3
  5. 2023MoniLog: An Automated Log-Based Anomaly Detection System for Cloud Computing Infrastructurescited by 2
  6. 2023Multi-View Person Matching and 3D Pose Estimation with Arbitrary Uncalibrated Camera Networkscited by 2
  7. 2021Artificial Intelligence in Dry Eye Diseasecited by 1
  8. 2021A comprehensive study of clustering a class of 2D shapescited by 1
  9. 2023Generating Multidimensional Clusters With Support Linescited by 1
  10. 2025Absolute Evaluation Measures for Machine Learning: A Surveycited by 1
  11. 2025Layout-Aware OCR for Black Digital Archives with Unsupervised Evaluationcited by 1
  12. 2025CURENet: Combining Unified Representations for Efficient Chronic Disease Predictioncited by 1
4 more
  1. 2025Mixed Data Clustering Survey and Challengescited by 1
  2. 2021Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlookcited by 0
  3. 2022Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategiescited by 0
  4. 2023Towards Unsupervised Representation Learning: Learning, Evaluating and Transferring Visual Representationscited by 0