• DocumentCode
    3601094
  • Title

    Twin Support Vector Machine for Clustering

  • Author

    Zhen Wang ; Yuan-Hai Shao ; Lan Bai ; Nai-Yang Deng

  • Author_Institution
    Sch. of Math. Sci., Inner Mongolia Univ., Hohhot, China
  • Volume
    26
  • Issue
    10
  • fYear
    2015
  • Firstpage
    2583
  • Lastpage
    2588
  • Abstract
    The twin support vector machine (TWSVM) is one of the powerful classification methods. In this brief, a TWSVM-type clustering method, called twin support vector clustering (TWSVC), is proposed. Our TWSVC includes both linear and nonlinear versions. It determines k cluster center planes by solving a series of quadratic programming problems. To make TWSVC more efficient and stable, an initialization algorithm based on the nearest neighbor graph is also suggested. The experimental results on several benchmark data sets have shown a comparable performance of our TWSVC.
  • Keywords
    graph theory; pattern classification; pattern clustering; quadratic programming; support vector machines; TWSVC; TWSVM-type clustering method; benchmark data sets; classification methods; initialization algorithm; k-cluster center planes; linear system; nearest neighbor graph; nonlinear system; quadratic programming problems; twin-support vector clustering; twin-support vector machine; Accuracy; Benchmark testing; Clustering algorithms; Clustering methods; Learning systems; Manifolds; Support vector machines; Manifold clustering; plane-based clustering; twin support vector machine (TWSVM); unsupervised learning; unsupervised learning.;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2014.2379930
  • Filename
    7001711