Abstract
Accurate identification of geospatial data has a fundamental impact on research and management of wildlife and animal behaviour. Consensus clustering literature neglects the fact that different algorithms are better suited to different dataset geometries, and this can be assessed prior to clustering. In this paper we propose the Spatial Meta-Feature Weighted (SMFW) Consensus Clustering algorithm, which improves the classic EAC for spatial animal data by replacing equal weighting with multi-algorithm adaptive weighting based on spatial meta-features, and by determining weights predictively without a training phase. In addition, this approach made it possible to remove the assumption regarding the number of clusters required by the methods used. Experiments on real and synthetic datasets for the primary ARI metric showed an average improvement of 22.5% compared to traditional approaches. The study's findings demonstrate that the proposed method is an effective tool for geospatial data clustering.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.198
Novel spatial meta-feature weighted consensus clustering of geospatial data
Accurate identification of geospatial data has a fundamental impact on research and management of wildlife and animal behaviour. Consensus clustering literature neglects the fact that different algorithms are better suited to different dataset geometries, and this can be assessed prior to clustering. In this paper we propose the Spatial Meta-Feature Weighted (SMFW) Consensus Clustering algorithm, which improves the classic EAC for spatial animal data by replacing equal weighting with multi-algorithm adaptive weighting based on spatial meta-features, and by determining weights predictively without a training phase. In addition, this approach made it possible to remove the assumption regarding the number of clusters required by the methods used. Experiments on real and synthetic datasets for the primary ARI metric showed an average improvement of 22.5% compared to traditional approaches. The study's findings demonstrate that the proposed method is an effective tool for geospatial data clustering.
Recommended Citation
Kozar, J. & Charytanowicz, M.(2026). Novel spatial meta-feature weighted consensus clustering of geospatial data. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.198