• DocumentCode
    769712
  • Title

    Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering

  • Author

    Jaewook Lee ; Daewon Lee

  • Author_Institution
    Dept. of Ind. & Manage. Eng., Pohang Univ. of Sci. & Technol.
  • Volume
    28
  • Issue
    11
  • fYear
    2006
  • Firstpage
    1869
  • Lastpage
    1874
  • Abstract
    A topological and dynamical characterization of the cluster structures described by the support vector clustering is developed. It is shown that each cluster can be decomposed into its constituent basin level cells and can be naturally extended to an enlarged clustered domain, which serves as a basis for inductive clustering. A simplified weighted graph preserving the topological structure of the clusters is also constructed and is employed to develop a robust and inductive clustering algorithm. Simulation results are given to illustrate the robustness and effectiveness of the proposed method
  • Keywords
    pattern clustering; support vector machines; cluster structures; constituent basin level cells; inductive support vector clustering; simplified weighted graph; Clustering algorithms; Clustering methods; Computational modeling; Kernel; Labeling; Machine learning; Robustness; Shape; Static VAr compensators; Support vector machines; Clustering; dynamical systems.; inductive learning; kernel methods; support vector machines; Algorithms; Artificial Intelligence; Cluster Analysis; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Pattern Recognition, Automated;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.2006.225
  • Filename
    1704842