# Agglomerative Likelihood Clustering

## TL;DR

This paper introduces a fast agglomerative clustering algorithm for large-scale time-series data that is resource-efficient, does not require prior cluster information, and effectively detects state changes in complex environments.

## Contribution

The paper presents a novel agglomerative likelihood clustering algorithm (ALC) that improves speed and resource efficiency over previous genetic algorithm-based methods for time-series clustering.

## Key findings

- Effective on large datasets with up to 20,000 assets
- Produces meaningful clusters in noisy synthetic data
- Reduces computational costs for large-scale clustering

## Abstract

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.