# A comprehensive study of clustering a class of 2D shapes

arXiv: https://arxiv.org/abs/2111.06662 · HTML: https://mlsanity.com/paper/2111.06662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 54 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Evaluation Metrics for Unsupervised Learning Algorithms](https://mlsanity.com/paper/1905.05667/) (2019)
- [Fr'echet Distance for Curves, Revisited](https://mlsanity.com/paper/1504.07685/) (2015)
- [Procrustes registration of two-dimensional statistical shape models without correspondences](https://mlsanity.com/paper/1911.11431/) (2019)

## Built on by (most cited first, 1 of 1)

- [On the application of the Wasserstein metric to 2D curves classification](https://mlsanity.com/paper/2601.07749/) (2026)

## Abstract (arXiv)

The paper concerns clustering with respect to the shape and size of 2D contours that are boundaries of cross-sections of 3D objects of revolution. We propose a number of similarity measures based on combined disparate Procrustes analysis (PA) and Dynamic Time Warping (DTW) distances. Motivation and the main application for this study comes from archaeology. The performed computational experiments refer to the clustering of archaeological pottery.