# funLOCI: a local clustering algorithm for functional data

arXiv: https://arxiv.org/abs/2305.12991 · HTML: https://mlsanity.com/paper/2305.12991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 22 May 2023.

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

- [Sparse Clustering of Functional Data](https://mlsanity.com/paper/1501.04755/) (2015)
- [Probabilistic K-mean with local alignment for clustering and motif discovery in functional data](https://mlsanity.com/paper/1808.04773/) (2018)
- [A novel framework for joint sparse clustering and alignment of functional data](https://mlsanity.com/paper/1912.00687/) (2019)

## Abstract (arXiv)

Nowadays, more and more problems are dealing with data with one infinite continuous dimension: functional data. In this paper, we introduce the funLOCI algorithm which allows to identify functional local clusters or functional loci, i.e., subsets/groups of functions exhibiting similar behaviour across the same continuous subset of the domain. The definition of functional local clusters leverages ideas from multivariate and functional clustering and biclustering and it is based on an additive model which takes into account the shape of the curves. funLOCI is a three-step algorithm based on divisive hierarchical clustering. The use of dendrograms allows to visualize and to guide the searching procedure and the cutting thresholds selection. To deal with the large quantity of local clusters, an extra step is implemented to reduce the number of results to the minimum.