• DocumentCode
    2375559
  • Title

    A Rough Set Approach for Clustering the Data Using Knowledge Discovery in World Wide Web for E-Business

  • Author

    Tripathy, Hrudaya Ku ; Tripathy, B.K.

  • Author_Institution
    Inst. of Adv. Comput. & Res., Rayagada
  • fYear
    2007
  • fDate
    24-26 Oct. 2007
  • Firstpage
    717
  • Lastpage
    722
  • Abstract
    Data mining and/or knowledge discovery is a very important part of today´s e-business. The World Wide Web has become in reality, the largest online information available practically to anyone with access to Internet. An e-business framework is proposed in the paper, as well as the knowledge discovery technique to personalize e-business, increase cross selling, and improve the customer relationship management. Due to the enormous size of the Web and low precision of user queries, results returned from present Web search engines can reach hundreds or even thousands data. Therefore, finding the right information can be difficult if not impossible. One approach that tries to solve this problem is by using clustering techniques for grouping similar data together in order to facilitate presentation of results in more compact form and enable browsing of the results set. In this paper, a data clustering techniques is presented with emphasis on application to Web search results. An algorithm for clustering Web data based on Rough Set is presented and its practical implementation is discussed.
  • Keywords
    Internet; data mining; electronic commerce; pattern clustering; rough set theory; Web data clustering; World Wide Web; data mining; e-business; knowledge discovery; rough set approach; Data mining; Databases; Information analysis; Internet; Knowledge management; Search engines; Technological innovation; Web search; Web server; Web sites; Data Mining; Knowledge Discovery; Rough set; WWW; clustering.;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    e-Business Engineering, 2007. ICEBE 2007. IEEE International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-0-7695-3003-1
  • Type

    conf

  • DOI
    10.1109/ICEBE.2007.62
  • Filename
    4402171