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Authors: Benjamin Ertl 1 ; Jörg Meyer 1 ; Achim Streit 1 and Matthias Schneider 2

Affiliations: 1 Steinbuch Centre for Computing (SCC), Karlsruhe Institute of Technology (KIT), Karlsruhe and Germany ; 2 Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology (KIT), Karlsruhe and Germany

ISBN: 978-989-758-382-7

ISSN: 2184-3228

Keyword(s): Machine Learning, Pattern Recognition, Clustering, Spatio-temporal Data, Mixtures of Gaussians, Climate Research.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Clustering and Classification Methods ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Structured Data Analysis and Statistical Methods ; Symbolic Systems

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 fe atures. (More)

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Paper citation in several formats:
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 - Volume 1: KDIR, ISBN 978-989-758-382-7, ISSN 2184-3228, pages 45-54. DOI: 10.5220/0007949700450054

@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 - Volume 1: KDIR,},
year={2019},
pages={45-54},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007949700450054},
isbn={978-989-758-382-7},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - 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

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