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

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

Keyword(s): Data Mining, Machine Learning, Pattern Recognition, Clustering, Correlation Clustering, Constrained Clustering, DBSCAN, Spatio-temporal Data, Climate Research.

Abstract: Full space clustering methods suffer the curse of dimensionality, for example points tend to become equidistant from one another as the dimensionality increases. Subspace clustering and correlation clustering algorithms overcome these issues, but still face challenges when data points have complex relations or clusters overlap. In these cases, clustering with constraints can improve the clustering results, by including a priori knowledge into the clustering process. This article proposes a new clustering algorithm CoExDBSCAN, density-based clustering with constrained expansion, which combines traditional, density-based clustering with techniques from subspace, correlation and constrained clustering. The proposed algorithm uses DBSCAN to find density-connected clusters in a defined subspace of features and restricts the expansion of clusters to a priori constraints. We provide verification and runtime analysis of the algorithm on a synthetic dataset and experimental evaluation on a cl imatology dataset of satellite observations. The experimental dataset demonstrates, that our algorithm is especially suited for spatio-temporal data, where one subspace of features defines the spatial extent of the data and another correlations between features. (More)

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Paper citation in several formats:
Ertl, B.; Meyer, J.; Schneider, M. and Streit, A. (2020). CoExDBSCAN: Density-based Clustering with Constrained Expansion. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, ISBN 978-989-758-474-9; ISSN 2184-3228, pages 104-115. DOI: 10.5220/0010131201040115

@conference{kdir20,
author={Benjamin Ertl. and Jörg Meyer. and Matthias Schneider. and Achim Streit.},
title={CoExDBSCAN: Density-based Clustering with Constrained Expansion},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,},
year={2020},
pages={104-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010131201040115},
isbn={978-989-758-474-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR,
TI - CoExDBSCAN: Density-based Clustering with Constrained Expansion
SN - 978-989-758-474-9
IS - 2184-3228
AU - Ertl, B.
AU - Meyer, J.
AU - Schneider, M.
AU - Streit, A.
PY - 2020
SP - 104
EP - 115
DO - 10.5220/0010131201040115