• DocumentCode
    2526923
  • Title

    Cluster utility: a new metric for clustering biological sequences

  • Author

    Lee, Jason ; Kim, Sun

  • Author_Institution
    Sch. of Informatics, Indiana Univ., Bloomington, IN, USA
  • fYear
    2005
  • fDate
    8-11 Aug. 2005
  • Firstpage
    45
  • Lastpage
    46
  • Abstract
    We propose cluster utility (CU), a metric that is based on consideration of similarity within a cluster and difference between clusters without metric space assumption. CU showed a very high correlation with the quality index. CU scales very well with data size and its strong correlation with quality index was nearly invariable regardless of data size change. CU can be used in two ways: to guide sequence clustering algorithms and to evaluate clustering results.
  • Keywords
    biology computing; genetics; graph theory; pattern clustering; statistical analysis; biological sequence clustering; cluster utility; quality index; Bioinformatics; Clustering algorithms; Extraterrestrial measurements; Genomics; Informatics; Sun;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE
  • Print_ISBN
    0-7695-2442-7
  • Type

    conf

  • DOI
    10.1109/CSBW.2005.38
  • Filename
    1540534
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2526923