Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/83028| Author(s): | T, HF João Gama |
| Title: | An eigenvector-based hotspot detection |
| Issue Date: | 2014 |
| Abstract: | Space and time are two critical components of many real world systems. For this reason, analysis of anomalies in spatiotemporal data has been a great of interest. In this work, application of tensor decomposition and eigenspace techniques on spa- tiotemporal hotspot detection is investigated. An algorithm called SST-Hotspot is proposed which accounts for spatiotemporal variations in data and detect hotspots using matching of eigenvector elements of two cases and population tensors. The experimental results reveal the interesting application of tensor decomposition and eigenvector-based techniques in hotspot analysis. |
| URI: | https://hdl.handle.net/10216/83028 |
| Document Type: | Artigo em Revista Científica Internacional |
| Rights: | openAccess |
| License: | https://creativecommons.org/licenses/by-nc/4.0/ |
| Appears in Collections: | FEP - Artigo em Revista Científica Internacional |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 115087.pdf | An eigenvector-based hotspot detectionVerificada pelos Serviços | 5.33 MB | Adobe PDF |  View/Open |
This item is licensed under a Creative Commons License
