• DocumentCode
    238722
  • Title

    An algorithm for scalable clustering: Ensemble Rapid Centroid Estimation

  • Author

    Yuwono, Mitchell ; Sir, Steven W. ; Moulton, Brace D. ; Ying Guo ; Nguyen, Hung T.

  • Author_Institution
    Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
  • fYear
    2014
  • fDate
    6-11 July 2014
  • Firstpage
    1250
  • Lastpage
    1257
  • Abstract
    This paper describes a new algorithm, called Ensemble Rapid Centroid Estimation (ERCE), designed to handle large-scale non-convex cluster optimization tasks, and estimate the number of clusters with quasi-linear complexity. ERCE stems from a recently developed Rapid Centroid Estimation (RCE) algorithm. RCE was originally developed as a lightweight simplification of the Particle Swarm Clustering (PSC) algorithm. RCE retained the quality of PSC, greatly reduced the computational complexity, and increased the stability. However, RCE has certain limitations with respect to complexity, and is unsuitable for non-convex clusters. The new ERCE algorithm presented here addresses these limitations.
  • Keywords
    computational complexity; concave programming; particle swarm optimisation; pattern clustering; ERCE algorithm; PSC algorithm; cluster number estimation; computational complexity reduction; ensemble rapid centroid estimation algorithm; large-scale nonconvex cluster optimization tasks; particle swarm clustering algorithm; quasilinear complexity; scalable clustering algorithm; Algorithm design and analysis; Clustering algorithms; Computational complexity; Estimation; Indexes; Particle swarm optimization; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2014 IEEE Congress on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-6626-4
  • Type

    conf

  • DOI
    10.1109/CEC.2014.6900295
  • Filename
    6900295