Mining and visualizing uncertain data objects and named data networking traffics by fuzzy self-organizing map
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/26239
Tipus de documentText en actes de congrés
Data publicació2014
EditorCEUR-WS.org
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
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.
CitacióKarami, A.; Guerrero, M. Mining and visualizing uncertain data objects and named data networking traffics by fuzzy self-organizing map. A: International Workshop on Artificial Intelligence and Cognition. "Proceedings of the Second International Workshop on Artificial Intelligence and Cognition (AIC 2014): Torino, Italy, November 26-27, 2014". Torino: CEUR-WS.org, 2014, p. 156-163.
ISSN1613-0073
Versió de l'editorhttp://ceur-ws.org/Vol-1315/paper14.pdf
Fitxers | Descripció | Mida | Format | Visualitza |
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Guerrero.pdf | 1,363Mb | Visualitza/Obre |