# Time Series Clustering via Community Detection in Networks

arXiv: https://arxiv.org/abs/1508.04757 · HTML: https://mlsanity.com/paper/1508.04757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2015.

## Built on by (most cited first, 5 of 5)

- [Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm](https://mlsanity.com/paper/2209.05757/) (2022)
- [Transfer learning for time series classification using synthetic data generation](https://mlsanity.com/paper/2207.07897/) (2022)
- [Trajectory Clustering Performance Evaluation: If we know the answer, it's not clustering](https://mlsanity.com/paper/2112.01570/) (2021)
- [Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering](https://mlsanity.com/paper/2310.11940/) (2023)
- [Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge](https://mlsanity.com/paper/2605.22540/) (2026)

## Abstract (arXiv)

In this paper, we propose a technique for time series clustering using community detection in complex networks. Firstly, we present a method to transform a set of time series into a network using different distance functions, where each time series is represented by a vertex and the most similar ones are connected. Then, we apply community detection algorithms to identify groups of strongly connected vertices (called a community) and, consequently, identify time series clusters. Still in this paper, we make a comprehensive analysis on the influence of various combinations of time series distance functions, network generation methods and community detection techniques on clustering results. Experimental study shows that the proposed network-based approach achieves better results than various classic or up-to-date clustering techniques under consideration. Statistical tests confirm that the proposed method outperforms some classic clustering algorithms, such as k-medoids, diana, median-linkage and centroid-linkage in various data sets. Interestingly, the proposed method can effectively detect shape patterns presented in time series due to the topological structure of the underlying network constructed in the clustering process. At the same time, other techniques fail to identify such patterns. Moreover, the proposed method is robust enough to group time series presenting similar pattern but with time shifts and/or amplitude variations. In summary, the main point of the proposed method is the transformation of time series from time-space domain to topological domain. Therefore, we hope that our approach contributes not only for time series clustering, but also for general time series analysis tasks.