• DocumentCode
    469044
  • Title

    Quantum Theory: The unified framework for FCM and QC algorithm

  • Author

    Li, Zhi-hua ; Wang, Shi-Tong

  • Author_Institution
    Souther Yangtze Univ., Wuxi
  • Volume
    3
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    1045
  • Lastpage
    1048
  • Abstract
    Clustering aims to study the instance distribution in scale-space. Its characteristics are very similar to the particle world in quantum mechanism. The probability wave function describes the distribution of particle, and the Schrodinger equation is the major methodology of solving for wave function when restricted boundary condition is given.Once wave function is confirmed, and the quantum potential serves as the clustering objective function to determine the location of particle distribution. In machine learning, this quantum mechanism implies that we can discover the grouping structures inherent in data. This is the key of quantum clustering, and is the same as the mechanism used in FCM algorithm. In FCM, via the key fuzzy similarity parameter is deduced by the wave function, a important predictability is proposed, which a cryptical wave function is found existing in FCM, finally, a quantum theory interpretation about FCM is presented in this paper.
  • Keywords
    Schrodinger equation; fuzzy set theory; learning (artificial intelligence); probability; quantum computing; quantum theory; Schrodinger equation; fuzzy similarity parameter; instance distribution; machine learning; particle distribution; probability wave function; quantum theory; restricted boundary condition; Algorithm design and analysis; Clustering algorithms; Notice of Violation; Pattern analysis; Pattern recognition; Quantum computing; Quantum mechanics; Schrodinger equation; Wave functions; Wavelet analysis; Interpretation; Quantum Clustering; Quantum Potential; Quantum Theory; Wave function;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1065-1
  • Electronic_ISBN
    978-1-4244-1066-8
  • Type

    conf

  • DOI
    10.1109/ICWAPR.2007.4421586
  • Filename
    4421586