# Interrelating Fruchterman-Reingold Graph Visualization and Agglomerative Clustering

arXiv: https://arxiv.org/abs/2609.35073 · HTML: https://mlsanity.com/paper/2609.35073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Clustering Algorithms: A Comparative Approach](https://mlsanity.com/paper/1612.08388/) (2016)
- [Further Generalizations of the Jaccard Index](https://mlsanity.com/paper/2110.09619/) (2021)
- [On Similarity](https://mlsanity.com/paper/2111.02803/) (2021)
- [Complex systems: features, similarity and connectivity](https://mlsanity.com/paper/1606.05400/) (2016)
- [Revisiting Agglomerative Clustering](https://mlsanity.com/paper/2005.07995/) (2020)

## Abstract (arXiv)

Graph visualization methods and agglomerative clustering have been frequently considered in data analysis and pattern recognition. Because these approaches are interrelated and complementary, it is of particular interest to investigate their associations. In this work, we study the possible relationship between the Fruchterman-Reingold graph visualization method and four types of agglomerative clustering adopting single- and complete-linkage, average, and Ward's linkage criteria. Three types of datasets have been considered in 2 and 10 dimensions, as well as the PCA projection of the latter to two dimensions. The results obtained suggest that the relationship between the methods considered did not vary much for the three types of data mentioned above. At the same time, the agglomerative methods tended to yield results that are mostly similar to each other, while presenting moderate similarity with the original data. The Fruchterman-Reingold visualization resulted similar to the original data, but exhibited relatively smaller similarity to the agglomerative methods.