# funOCLUST: Clustering Functional Data with Outliers

arXiv: https://arxiv.org/abs/2508.00110 · HTML: https://mlsanity.com/paper/2508.00110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Functional Isolation Forest](https://mlsanity.com/paper/1904.04573/) (2019)
- [Finding Outliers in Gaussian Model-Based Clustering](https://mlsanity.com/paper/1907.01136/) (2019)
- [Outlier detection in multivariate functional data through a contaminated mixture model](https://mlsanity.com/paper/2106.07222/) (2021)
- [Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling](https://mlsanity.com/paper/1611.01602/) (2016)

## Abstract (arXiv)

Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers. The methodology is evaluated on both simulated and real-world functional datasets, demonstrating strong performance in clustering and outlier identification.