• DocumentCode
    506582
  • Title

    An approach to the construction of event archipelago for chance discovery via genetic algorithm based on correlation matrix

  • Author

    Zhang, Zhenya ; Zhang, Lin ; Zhang, Shuguan

  • Author_Institution
    Key Lab. of Intell. Building, Anhui Univ. of Archit., Hefei, China
  • Volume
    1
  • fYear
    2009
  • fDate
    20-22 Nov. 2009
  • Firstpage
    382
  • Lastpage
    386
  • Abstract
    Chance discovery is a new research topic on cognition psychology inspired deep data analysis. Scene is the contact point of human process and computer process in the double helical model of chance discovery. Event archipelago is the basic foundation for scene construction. Event archipelago and island are defined in this paper. Problem of event archipelago for Chance Discovery based on correlation matrix is a NP hard problem and to solve this problem instantly, GeneticCEA, an approximate algorithm based on genetic algorithm is presented. This paper discusses the performance of GeneticCEA too. Experimental results show that GeneticCEA can run with excellent performance for clustering task while it is treated as a kind of task for the construction of event archipelago in chance discovery.
  • Keywords
    computational complexity; data mining; genetic algorithms; GeneticCEA algorithm; NP-hard problem; chance discovery; cognition psychology; correlation matrix; deep data analysis; event archipelago construction; genetic algorithm; Clustering algorithms; Competitive intelligence; Data mining; Genetic algorithms; Humans; Image analysis; Intelligent structures; Layout; Psychology; Technology forecasting; Chance Discovery; Genetic Algorithm; Scene;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-4754-1
  • Electronic_ISBN
    978-1-4244-4738-1
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2009.5357820
  • Filename
    5357820