• DocumentCode
    447568
  • Title

    Consideration on hierarchical cluster analysis based on connecting adjacent hyper-rectangles

  • Author

    Yanagida, Ryoshin ; Takagi, Noboru

  • Author_Institution
    Dept. of Electron. & Informatics, Toyama Prefectural Univ., Japan
  • Volume
    3
  • fYear
    2005
  • fDate
    10-12 Oct. 2005
  • Firstpage
    2795
  • Abstract
    This paper proposes a new clustering method based on connecting adjacent hyper-rectangles. The k-means clustering is one of the well-known clustering techniques. At first, many clustering methods must decide the number of clusters. Our method searches a set of hyper-rectangles that satisfies the properties (1) each hyper-rectangle covers some of the samples, and (2) each sample is covered by at least one of the hyper-rectangles. Then, a collection of connected hyper-rectangles is assumed to be a cluster. One of the characteristic features of our method is that it can work if there is no initial value on the number of clusters assumed. We apply the hierarchical clustering method to realize the clustering based on connecting adjacent hyper-rectangles. The effectiveness of the, proposed method is shown by applying a small artificial data and iris data.
  • Keywords
    computational geometry; pattern clustering; sampling methods; unsupervised learning; adjacent hyper-rectangles; hierarchical cluster analysis; iris data; k-means clustering; Clustering algorithms; Clustering methods; Informatics; Iris; Joining processes; Optimization methods; Pattern analysis; Pattern classification; Rough sets; Statistical analysis; clustering; combinatorial optimization problem; hierarchical clustering; pattern classification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2005 IEEE International Conference on
  • Print_ISBN
    0-7803-9298-1
  • Type

    conf

  • DOI
    10.1109/ICSMC.2005.1571573
  • Filename
    1571573