• DocumentCode
    690265
  • Title

    A text mining model based on improved density clustering algorithm

  • Author

    Chen Qi ; Lu JianFeng ; Zhang Hao

  • Author_Institution
    CIMS Center of Tongji Univ., Shanghai, China
  • fYear
    2013
  • fDate
    15-17 Nov. 2013
  • Firstpage
    337
  • Lastpage
    339
  • Abstract
    The clustering algorithm based on density is widely used on text mining model, for example the DBSCAN(density-based spatial clustering of application with noise) algorithm. DBSCAN algorithm is sensitive in choose of parameters, it is hard to find suitable parameters. In this paper a method based on k-means algorithm is introduced to estimate the ε neighborhood and minpts. Finally an example is given to show the effectiveness of this algorithm.
  • Keywords
    data mining; pattern clustering; text analysis; DBSCAN; density based spatial clustering of application with noise algorithm; improved density clustering algorithm; k-means algorithm; text mining model; Art; Bayes methods; Clustering algorithms; Support vector machines; clustering algorithm; density clustering; text mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics Information and Emergency Communication (ICEIEC), 2013 IEEE 4th International Conference on
  • Conference_Location
    Beijing
  • Type

    conf

  • DOI
    10.1109/ICEIEC.2013.6835520
  • Filename
    6835520
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=690265