• DocumentCode
    1791858
  • Title

    Biclustering using Spark-MapReduce

  • Author

    Sarazin, Tugdual ; Lebbah, Mustapha ; Azzag, Hanane

  • Author_Institution
    LIPN, Univ. of Paris, Villetaneuse, France
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    58
  • Lastpage
    60
  • Abstract
    Biclustering approaches are more complex compared to the traditional clustering particularly those requiring large dataset and Mapreduce platforms. We propose a new approach of biclustering based on popular self-organizing maps, which is one of the famous unsupervised learning algorithms. We have designed scalable implementations of the new topological biclustering algorithm using MapReduce with the Spark platform.
  • Keywords
    parallel programming; pattern clustering; unsupervised learning; Spark-MapReduce platform; large-datasets; self-organizing maps; topological biclustering algorithm; unsupervised learning algorithms; Algorithm design and analysis; Clustering algorithms; Data mining; Distributed databases; Prototypes; Sparks; Vectors; Biclustering; Map-Reduce Spark; Self-organizing Map;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004493
  • Filename
    7004493
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1791858