• DocumentCode
    1621938
  • Title

    Spectral approach to find number of clusters of short-text documents

  • Author

    Goyal, Ankur ; Jadon, Mukesh K. ; Pujari, Arun K.

  • Author_Institution
    LNM Inst. of Inf. Technol., Jaipur, India
  • fYear
    2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    We propose a technique of determining the number of clusters of a corpus of short-text documents. A spectral algorithm suitable for short-texts is used to generate an ensemble. A Markov chain induced by the co-association matrix is studied to observe nearly uncoupling phenomenon over iterations. A large spectral gap and number of eigenvectors close to 1 indicate the number of clusters. We demonstrate by experimenting on several datasets.
  • Keywords
    Markov processes; eigenvalues and eigenfunctions; learning (artificial intelligence); matrix algebra; pattern clustering; text analysis; Markov chain; cluster number determination; coassociation matrix; eigenvectors; ensemble learning; short-text documents; spectral approach; spectral gap; Clustering algorithms; Data mining; Eigenvalues and eigenfunctions; Electronic mail; Feature extraction; Markov processes; Visualization; number of clusters; short-texts; spectral method; term-weighting; uncoupling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference on
  • Conference_Location
    Jodhpur
  • Print_ISBN
    978-1-4799-1586-6
  • Type

    conf

  • DOI
    10.1109/NCVPRIPG.2013.6776152
  • Filename
    6776152