The control of communication networks is an important aspect from both the service provider and user points of view. There are several approaches to communication network control including game theory, genetic algorithms and Markov decision processes. Data mining methods have been successfully used to discover optimized solutions to this problem, and have the capability to learn the network behavior under different network conditions and during operation so that complete knowledge of the network behavior is not required a priori. This article identifies the concepts behind the idea of using data mining for communication network control, provides a structured survey of the results in this area, and discusses the guidelines for future applications.

Data Mining Algorithms for Communication Networks Control: Concepts, Survey and Guidelines

ARANITI, Giuseppe
2016-01-01

Abstract

The control of communication networks is an important aspect from both the service provider and user points of view. There are several approaches to communication network control including game theory, genetic algorithms and Markov decision processes. Data mining methods have been successfully used to discover optimized solutions to this problem, and have the capability to learn the network behavior under different network conditions and during operation so that complete knowledge of the network behavior is not required a priori. This article identifies the concepts behind the idea of using data mining for communication network control, provides a structured survey of the results in this area, and discusses the guidelines for future applications.
2016
Algorithm design and analysis, Association rules, Clustering algorithms, Communication networks, Data models, Itemsets
File in questo prodotto:
File Dimensione Formato  
De_Sanctis_2016_IEEE_Network_Data_Editor.pdf

non disponibili

Tipologia: Versione Editoriale (PDF)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 160.19 kB
Formato Adobe PDF
160.19 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/6717
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 26
  • ???jsp.display-item.citation.isi??? 18
social impact