CoExDBSCAN: Density-based Clustering with Constrained Expansion

Benjamin Ertl, Jörg Meyer, Matthias Schneider, Achim Streit

2020

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 climatology 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.

Paper Citation

in Harvard Style

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 (IC3K 2020) - Volume 1: KDIR; ISBN 978-989-758-474-9, SciTePress, pages 104-115. DOI: 10.5220/0010131201040115

in Bibtex Style

@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 (IC3K 2020) - Volume 1: KDIR},
year={2020},
pages={104-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010131201040115},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 1: KDIR
TI - CoExDBSCAN: Density-based Clustering with Constrained Expansion
SN - 978-989-758-474-9
AU - Ertl B.
AU - Meyer J.
AU - Schneider M.
AU - Streit A.
PY - 2020
SP - 104
EP - 115
DO - 10.5220/0010131201040115
PB - SciTePress