# Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis

arXiv: https://arxiv.org/abs/2302.06228 · HTML: https://mlsanity.com/paper/2302.06228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 64 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [On the Reliable Detection of Concept Drift from Streaming Unlabeled Data](https://mlsanity.com/paper/1704.00023/) (2017)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection](https://mlsanity.com/paper/2103.00903/) (2021)
- [Learning Parameter Distributions to Detect Concept Drift in Data Streams](https://mlsanity.com/paper/2010.09388/) (2020)
- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [Handling Adversarial Concept Drift in Streaming Data](https://mlsanity.com/paper/1803.09160/) (2018)

## Built on by (most cited first, 3 of 3)

- [AI on the Pulse: Real-Time Health Anomaly Detection with Wearable and Ambient Intelligence](https://mlsanity.com/paper/2508.03436/) (2025)
- [Seamless Monitoring of Stress Levels Leveraging a Universal Model for Time Sequences](https://mlsanity.com/paper/2407.03821/) (2024)
- [Agent-Based Output Drift Detection for Breast Cancer Response Prediction in a Multisite Clinical Decision Support System](https://mlsanity.com/paper/2512.18450/) (2025)

## Abstract (arXiv)

Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, anomaly detection algorithms have been proposed for the early detection of abnormalities. Gradual change procedures, commonly referred to as drift anomalies, have received much less attention in the literature because they represent a much more challenging scenario than sudden temporary changes (point anomalies). In this paper, we propose, for the first time, a fully unsupervised real-time drift detection algorithm named DynAmo, which can identify drift periods as they are happening. DynAmo comprises a dynamic clustering component to capture the overall trends of monitored behaviours and a trajectory generation component, which extracts features from the densest cluster centroids. Finally, we apply an ensemble of divergence tests on sliding reference and detection windows to detect drift periods in the behavioural sequence.