Application of Mixtures of Gaussians for Tracking Clusters in Spatio-temporal Data

Benjamin Ertl, Jörg Meyer, Achim Streit, Matthias Schneider

2019

Abstract

Clustering data based on their spatial and temporal similarity has become a research area with increasing popularity in the field of data mining and data analysis. However, most clustering models for spatio-temporal data introduce additional complexity to the clustering process as well as scalability becomes a significant issue for the analysis. This article proposes a data-driven approach for tracking clusters with changing properties over time and space. The proposed method extracts cluster features based on Gaussian mixture models and tracks their spatial and temporal changes without incorporating them into the clustering process. This approach allows the application of different methods for comparing and tracking similar and changing cluster properties. We provide verification and runtime analysis on a synthetic dataset and experimental evaluation on a climatology dataset of satellite observations demonstrating a performant method to track clusters with changing spatio-temporal features.

Paper Citation

in Harvard Style

Ertl B., Meyer J., Streit A. and Schneider M. (2019). Application of Mixtures of Gaussians for Tracking Clusters in Spatio-temporal Data. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 45-54. DOI: 10.5220/0007949700450054

in Bibtex Style

@conference{kdir19,
author={Benjamin Ertl and Jörg Meyer and Achim Streit and Matthias Schneider},
title={Application of Mixtures of Gaussians for Tracking Clusters in Spatio-temporal Data},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={45-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007949700450054},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Application of Mixtures of Gaussians for Tracking Clusters in Spatio-temporal Data
SN - 978-989-758-382-7
AU - Ertl B.
AU - Meyer J.
AU - Streit A.
AU - Schneider M.
PY - 2019
SP - 45
EP - 54
DO - 10.5220/0007949700450054
PB - SciTePress