• DocumentCode
    3216009
  • Title

    Review on Ant Miners

  • Author

    Panchal, V.K. ; Singh, Poonam ; Narula, Appoorv ; Mishra, Ashutosh

  • Author_Institution
    DTRL Metcalfe House, Delhi, India
  • fYear
    2009
  • fDate
    9-11 Dec. 2009
  • Firstpage
    1641
  • Lastpage
    1644
  • Abstract
    Extracting classification rules from data is an important task of data mining and is gaining considerable attention in recent years. This paper comprises classification of different types of rule extraction algorithm and their comparative study by considering their advantages separately. These Ant Colony based algorithms called as Ant_Miner have been successfully implemented in various fields such as remote sensing problems, combinatorial problems, scheduling problems and the quadratic assignment problem. No single algorithm is efficient enough to tackle related problems arising from different fields. Hence, in this paper we present several Ant_Miner algorithms which can be used according to one´s need.
  • Keywords
    data mining; feature extraction; knowledge based systems; optimisation; pattern classification; Ant_Miner algorithm; ant colony optimization; classification rules extraction; data mining; rule extraction algorithm; Ant colony optimization; Artificial intelligence; Chemicals; Data mining; Image classification; Insects; Particle swarm optimization; Remote sensing; Satellites; Scheduling algorithm; Ant Colony Optimization (ACO); Ant_Miner; Classification of rules; Image Classification; data mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4244-5053-4
  • Type

    conf

  • DOI
    10.1109/NABIC.2009.5393635
  • Filename
    5393635