• DocumentCode
    660870
  • Title

    Finding Number of Clusters in a Gene Co-expression Network Using Independent Sets

  • Author

    Pirim, Harun

  • Author_Institution
    Fac. of Coll. of Comput. Sci. & Eng., KFUPM, Dhahran, Saudi Arabia
  • fYear
    2013
  • fDate
    8-14 Sept. 2013
  • Firstpage
    836
  • Lastpage
    839
  • Abstract
    Determining the number of clusters is required for most of the clustering algorithms. The number of clusters in a gene co-expression network is not known a prior. In this study, maximum independent set concept from graph theory is applied for a gene expression data set. The results indicate that employing independent set approach to approximate the number of clusters is promising.
  • Keywords
    biology computing; data analysis; graph theory; pattern clustering; set theory; clustering algorithms; gene co-expression network; gene expression data set; graph theory; maximum independent set concept; Approximation algorithms; Clustering algorithms; Data mining; Estimation; Gene expression; Histograms; clustering; independent sets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Social Computing (SocialCom), 2013 International Conference on
  • Conference_Location
    Alexandria, VA
  • Type

    conf

  • DOI
    10.1109/SocialCom.2013.125
  • Filename
    6693422
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=660870