Distribution Controlled Clustering of Time Series Segments by Reduced Embeddings

Gábor Szűcs, Marcell Balázs Tóth, Marcell Németh

2025

Abstract

This paper introduces a novel framework for clustering time series segments, addressing challenges like temporal misalignment, varying segment lengths, and computational inefficiencies. The method combines the Kolmogorov–Smirnov (KS) test for statistical segment comparison and adapted COP-KMeans for clustering with temporal constraints. To enhance scalability, we propose a basepoint selection strategy for embedding the time series segments that reduces the computational complexity from O(n2) to O(n · b) by limiting comparisons to representative basepoints. The approach is evaluated on diverse time series datasets from domains such as motion tracking and medical signals. Results show improved runtime performance over traditional methods, particularly for large datasets. In addition, we introduce a confidence score to quantify the reliability of cluster assignments, with higher accuracy achieved by filtering low-confidence segments. We evaluated clustering performance using the Rand Index (RI), Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI). Our results demonstrate advantageous properties of the method in handling noise and different time series data, making it suitable for large scale applications.

Paper Citation

in Harvard Style

Szűcs G., Tóth M. and Németh M. (2025). Distribution Controlled Clustering of Time Series Segments by Reduced Embeddings. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 43-54. DOI: 10.5220/0013162700003905

in Bibtex Style

@conference{icpram25,
author={Gábor Szűcs and Marcell Tóth and Marcell Németh},
title={Distribution Controlled Clustering of Time Series Segments by Reduced Embeddings},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={43-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013162700003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Distribution Controlled Clustering of Time Series Segments by Reduced Embeddings
SN - 978-989-758-730-6
AU - Szűcs G.
AU - Tóth M.
AU - Németh M.
PY - 2025
SP - 43
EP - 54
DO - 10.5220/0013162700003905
PB - SciTePress