• DocumentCode
    3570918
  • Title

    Finding the most evident co-clusters on web log dataset using frequent super-sequence mining

  • Author

    Xinran Yu ; Korkmaz, Turgay

  • Author_Institution
    Comput. Sci. Dept., Univ. of Texas at San Antonio, San Antonio, TX, USA
  • fYear
    2014
  • Firstpage
    529
  • Lastpage
    536
  • Abstract
    It is important to mine the weblog dataset to find interesting and helpful information. There are three kinds of mining on weblog data which are web usage mining, web structure mining and web content mining. In our research, we are going to investigate web pages structure and find the most evident groups of users and web pages. Nowadays, big data is everywhere. Facing huge amount of web logs, it is not always necessary to group all the users in a web log dataset into different clusters, sometimes, finding out the major dominant user groups and the corresponding web pages is more important. In this paper, we are going to investigate a new way to search the most evident co-clusters of users and the corresponding web pages in the web log dataset using frequent super-sequence mining technique. Through experiments we find interesting results.
  • Keywords
    Web sites; data mining; pattern clustering; Web content mining; Web log dataset mining; Web page structure; Web structure mining; Web usage mining; frequent super-sequence mining technique; most evident user coclusters; Clustering algorithms; Data mining; Databases; Market research; Merging; Phase change materials; Web pages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on
  • Type

    conf

  • DOI
    10.1109/IRI.2014.7051935
  • Filename
    7051935