# A time resolved clustering method revealing longterm structures and their short-term internal dynamics

arXiv: https://arxiv.org/abs/1912.04261 · HTML: https://mlsanity.com/paper/1912.04261/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

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

- [MCbiF: Measuring Topological Autocorrelation in Multiscale Clusterings via 2-Parameter Persistent Homology](https://mlsanity.com/paper/2510.14710/) (2025)
- [Temporal distribution of clusters of investors and their application in prediction with expert advice](https://mlsanity.com/paper/2406.19403/) (2024)

## Abstract (arXiv)

The last decades have not only been characterized by an explosive growth of data, but also an increasing appreciation of data as a valuable resource. Their value comes with the ability to extract meaningful patterns that are of economic, societal or scientific relevance. A particular challenge is the identification of patterns across time, including those that might only become apparent when the temporal dimension is taken into account. Here, we present a novel method that aims to achieve this by detecting dynamic clusters, i.e. structural elements that can be present over prolonged durations. It is based on an adaptive identification of majority overlaps between groups at different time points and accommodates the transient decompositions in otherwise persistent dynamic clusters. Our method enables the detection of persistent structural elements with internal dynamics and can be applied to any classifiable data, ranging from social contact networks to arbitrary sets of time stamped feature vectors. It represents a unique tool to study systems with non-trivial temporal dynamics and has a broad applicability to scientific, societal and economic data.