• DocumentCode
    3169118
  • Title

    Mining E-Commerce Data to Analyze the Target Customer Behavior

  • Author

    Jiang, Yuantao ; Yu, Siqin

  • Author_Institution
    Shanghai Maritime Univ., Shanghai
  • fYear
    2008
  • fDate
    23-24 Jan. 2008
  • Firstpage
    406
  • Lastpage
    409
  • Abstract
    In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers´ needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using k-means algorithm and data from Web log of various e-commerce Websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.
  • Keywords
    customer satisfaction; data mining; electronic commerce; Web log; e-commerce data mining; forecasting methods; k-means algorithm; Clustering algorithms; Data analysis; Data mining; Databases; Electronic commerce; HTML; Information analysis; Information technology; Navigation; Production facilities;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on
  • Conference_Location
    Adelaide, SA
  • Print_ISBN
    978-0-7695-3090-1
  • Type

    conf

  • DOI
    10.1109/WKDD.2008.90
  • Filename
    4470425