Publicação
DeepPAY: A Framework for Dimensionality Reduction and Interactive Exploration
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Bação, Fernando José Ferreira Lucas | |
| dc.contributor.author | Silva, Diana Ferreira da | |
| dc.date.accessioned | 2025-11-14T15:51:35Z | |
| dc.date.available | 2025-11-14T15:51:35Z | |
| dc.date.issued | 2025-10-31 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science | pt_PT |
| dc.description.abstract | As payments become more digitalized and interconnected, the complexity and volume of the data they generate continue to grow, resulting in new challenges for analysis and interpretation. These challenges are critical in contexts where timely insight and transparency are essential. This study introduces a visual analytics framework designed to explore highdimensional payment data through a combination of dimensionality reduction (PCA and UMAP), clustering techniques, and an autoencoder–isolation forest model for anomaly detection. The solution is implemented as a modular Dash application that supports dynamic interaction, enabling users to uncover latent structures, identify behavioural clusters, and detect anomalous or inconsistent records. Two usage scenarios are explored, using payments data collected by Banco de Portugal: one that examines the clustering patterns and network topology of interbank transactions, and another that focuses on detecting abrupt changes and irregularities in paymentseries. Together, these use cases demonstrate the system’s potential to support both structural and temporal analyses. Although the framework was developed with a focus on payment systems, the approach is sufficiently general to be applied to other domains involving high-dimensional transactional information, such as stock exchange operations or insurance records. By bringing together analytical depth and interpretability, this work contributes to the design of transparent and flexible tools for navigating complex data ecosystems. | pt_PT |
| dc.identifier.tid | 204071348 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190750 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | anomaly detection | pt_PT |
| dc.subject | clustering | pt_PT |
| dc.subject | dash application | pt_PT |
| dc.subject | high-dimensional data | pt_PT |
| dc.subject | network analysis | pt_PT |
| dc.subject | payment data | pt_PT |
| dc.title | DeepPAY: A Framework for Dimensionality Reduction and Interactive Exploration | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Data Science | pt_PT |