• DocumentCode
    2550959
  • Title

    Semi-supervised subtractive clustering by seeding

  • Author

    Gu, Lei ; Lu, Xianling

  • Author_Institution
    Key Lab. of Adv. Process Control for Light Ind., Jiangnan Univ., Wuxi, China
  • fYear
    2012
  • fDate
    29-31 May 2012
  • Firstpage
    738
  • Lastpage
    741
  • Abstract
    In this paper, a novel semi-supervised subtractive clustering algorithm by seeding is proposed. Like the semi-supervised clustering approaches based on K-Means, the presented method applies a small amount of labeled data called seeds to aid the traditional subtractive clustering. Experimental results show that the new method can improve the clustering performance significantly compared to other semi-supervised clustering algorithms.
  • Keywords
    learning (artificial intelligence); pattern clustering; SSCS; clustering performance improvement; k-means clustering; labeled data; semisupervised subtractive clustering by seeding; subtractive clustering; Accuracy; Clustering algorithms; Clustering methods; Educational institutions; Ionosphere; Laboratories; K-Means; seeds; semi-suprvised clustering; subtractive clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
  • Conference_Location
    Sichuan
  • Print_ISBN
    978-1-4673-0025-4
  • Type

    conf

  • DOI
    10.1109/FSKD.2012.6234240
  • Filename
    6234240
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2550959