• DocumentCode
    2086602
  • Title

    Research of Cluster-Based Data Mining Techniques in E-Commerce

  • Author

    Huang Weijian ; Zhou Xuqian

  • Author_Institution
    Hebei Univ. of Eng., Handan, China
  • fYear
    2009
  • fDate
    20-22 Sept. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The data mining in electronic commerce is mainly Web data excavation; this article introduces the general process of Web excavation, studies the k-means cluster algorithm and analyzes some insufficiencies in k-means cluster algorithm. On basis of that, an improved algorithm of k-means cluster algorithm is proposed which can enhance the recommendation speed by compressing the size of recommendation pond, thus enhance the cluster efficiency.
  • Keywords
    Web services; data mining; electronic commerce; Web data excavation; cluster-based data mining; e-commerce; electronic commerce; k-means cluster algorithm; Algorithm design and analysis; Clustering algorithms; Collaboration; Data engineering; Data mining; Electronic commerce; Information filtering; Information filters; Nearest neighbor searches; Partitioning algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management and Service Science, 2009. MASS '09. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4638-4
  • Electronic_ISBN
    978-1-4244-4639-1
  • Type

    conf

  • DOI
    10.1109/ICMSS.2009.5301539
  • Filename
    5301539
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2086602