# Agglomerative Likelihood Clustering

arXiv: https://arxiv.org/abs/1908.00951 · HTML: https://mlsanity.com/paper/1908.00951/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Representation Learning for Regime detection in Block Hierarchical Financial Markets](https://mlsanity.com/paper/2410.22346/) (2024)
- [a-TMFG: Scalable Triangulated Maximally Filtered Graphs via Approximate Nearest Neighbors](https://mlsanity.com/paper/2603.09564/) (2026)

## Abstract (arXiv)

We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive Agglomerative Likelihood Clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in Econophysics and Community Detection. The method is tested on noisy synthetic correlated time-series data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We apply it to time-series data-sets as large as 20,000 assets and we argue that ALC can reduce compute time costs and resource usage cost for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment because the algorithm requires no prior information about the number of clusters.