Clustering of Crimes Using Latent Representations Obtained via Autoencoders
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
| Status: | |
| Autorzy: | Nadworska Weronika, Piłat-Rożek Magdalena, Łazuka Ewa |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 14 |
| Wolumen/Tom: | 16 |
| Numer artykułu: | 7351 |
| Strony: | 1 - 29 |
| Impact Factor: | 2,9 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was funded by grants from the Polish Ministry of Science and Higher Education: FD-20/I´S-6/047, FD-20/DN-10/037, FD-20/DN-10/999 and FJ-KMS. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 22 lipca 2026 |
| Abstrakty: | angielski |
| This article presents the use of autoencoders as part of a dimensionality-reduction method in the task of crime clustering. The study was conducted on a real-world crime dataset from the city of Chicago, based on publicly available police records. The initial data processing involved selecting and extracting variables, aggregating the selected variables, and converting crime categories and incident locations into contextual embeddings. The data prepared in this way was used to train various autoencoder architectures, including Vanilla, convolutional, denoising and variational models. The representations obtained from the latent layer of the encoder were then used as input data for clustering methods, such as k-means, Gaussian mixture model, and spectral clustering. The experimental results showed that the use of autoencoders in the clustering process enabled the identification of distinct groups of offences, with the best results (measured using the ARI and NMI metrics) obtained for Vanilla autoencoders combined with k-means and GMM, particularly with intermediate latent-space dimensions. The results confirm the potential of autoencoders as effective tools for dimensionality reduction and feature extraction in crime data analysis, as well as their usefulness in the exploratory analysis of complex urban data. |
