# A principled methodology for comparing relatedness measures for clustering publications

arXiv: https://arxiv.org/abs/1901.06815 · HTML: https://mlsanity.com/paper/1901.06815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Data Clustering and Visualization with Recursive Goemans-Williamson MaxCut Algorithm](https://mlsanity.com/paper/2408.07763/) (2024)
- [Similarity-based fuzzy clustering scientific articles: potentials and challenges from mathematical and computational perspectives](https://mlsanity.com/paper/2506.04045/) (2025)
- [Constraint-Driven Small Language Models Based on Agent and OpenAlex Knowledge Graph: Mining Conceptual Pathways and Discovering Innovation Points in Academic Papers](https://mlsanity.com/paper/2510.14303/) (2025)
- [Mapping the Web of Science, a large-scale graph and text-based dataset with LLM embeddings](https://mlsanity.com/paper/2602.04630/) (2026)

## Abstract (arXiv)

There are many different relatedness measures, based for instance on citation relations or textual similarity, that can be used to cluster scientific publications. We propose a principled methodology for evaluating the accuracy of clustering solutions obtained using these relatedness measures. We formally show that the proposed methodology has an important consistency property. The empirical analyses that we present are based on publications in the fields of cell biology, condensed matter physics, and economics. Using the BM25 text-based relatedness measure as evaluation criterion, we find that bibliographic coupling relations yield more accurate clustering solutions than direct citation relations and co-citation relations. The so-called extended direct citation approach performs similarly to or slightly better than bibliographic coupling in terms of the accuracy of the resulting clustering solutions. The other way around, using a citation-based relatedness measure as evaluation criterion, BM25 turns out to yield more accurate clustering solutions than other text-based relatedness measures.