Mining and visualizing uncertain data objects and named data networking traffics by fuzzy self-organizing map
| Autor: | Karami, A., Manel Guerrero Zapata |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. CNDS - Xarxes de Computadors i Sistemes Distribuïts |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Sensor networks
Artificial intelligence Fuzzy sets Named data networkings Incertesa -- Models matemàtics Fuzzy self-organizing maps Xarxes de sensors Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat [Àrees temàtiques de la UPC] Conformal mapping Performance criterion Uncertain datas Real-world Uncertainty -- Mathematical models Hybrid algorithms Bench-mark problems Uncertain database Algorithms Self organizing maps |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya Universitat Jaume I UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Scopus-Elsevier |
| Popis: | Uncertainty is widely spread in real-world data. Uncertain data-in computer science-is typically found in the area of sensor networks where the sensors sense the environment with certain error. Mining and visualizing uncertain data is one of the new challenges that face uncertain databases. This paper presents a new intelligent hybrid algorithm that applies fuzzy set theory into the context of the Self-Organizing Map to mine and visualize uncertain objects. The algorithm is tested in some benchmark problems and the uncertain traffics in Named Data Networking (NDN). Experimental results indicate that the proposed algorithm is precise and effective in terms of the applied performance criteria. |
| Databáze: | OpenAIRE |
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