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Web Mining: A Survey Of Current Research, Techniques, And Software

Author

Listed:
  • QINGYU ZHANG

    (Department of Computer & Information Technology, Arkansas State University, State University, Arkansas 72467-0130, USA)

  • RICHARD S. SEGALL

    (Department of Computer & Information Technology, Arkansas State University, State University, Arkansas 72467-0130, USA)

Abstract

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

Suggested Citation

  • Qingyu Zhang & Richard S. Segall, 2008. "Web Mining: A Survey Of Current Research, Techniques, And Software," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 683-720.
    • Handle: RePEc:wsi:ijitdm:v:07:y:2008:i:04:n:s0219622008003150
    DOI: 10.1142/S0219622008003150
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    Citations

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    Cited by:

    1. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.
    2. Gang Kou & Chunwei Lou, 2012. "Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data," Annals of Operations Research, Springer, vol. 197(1), pages 123-134, August.
    3. Andrea Ko & Saira Gillani, 2020. "A Research Review and Taxonomy Development for Decision Support and Business Analytics Using Semantic Text Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 97-126, January.

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