• DocumentCode
    3540177
  • Title

    Evolving email clustering method for email grouping: A machine learning approach

  • Author

    Ayodele, Taiwo ; Zhou, Shikun ; Khusainov, Rinat

  • Author_Institution
    Dept. of Electron. & Comput. Eng., Univ. of Portsmouth, Portsmouth, UK
  • fYear
    2009
  • fDate
    4-6 Aug. 2009
  • Firstpage
    357
  • Lastpage
    362
  • Abstract
    This paper presents the design and implementation of a new system to manage email messages using email evolving clustering method with unsupervised learning approach to group emails base on activities found in the email messages, namely email grouping. Users spend a lot of time reading, replying and organizing their emails. To help users organize their email messages, we propose a new framework to help organise and prioritize email better. The goal is to provide highly structured and prioritized emails, thus saving the user from browsing through each email one by one and help to save time.
  • Keywords
    electronic mail; pattern clustering; unsupervised learning; email clustering method; email grouping; email messages; machine learning approach; unsupervised learning approach; Clustering methods; Design engineering; Electrochemical machining; Engineering management; Machine learning; Mission critical systems; Organizing; Postal services; Telephony; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the
  • Conference_Location
    London
  • Print_ISBN
    978-1-4244-4456-4
  • Electronic_ISBN
    978-1-4244-4457-1
  • Type

    conf

  • DOI
    10.1109/ICADIWT.2009.5273973
  • Filename
    5273973