Newport Beach, CA
Nov. 4, 1997 to Nov. 4, 1997
R. Cooley , University of Minnesota
B. Mobasher , University of Minnesota
J. Srivastava , University of Minnesota
Web-based organizations often generate and collect large volumes of data in their daily operations. Analyzing such data involves the discovery of meaningful relationships from a large collection of primarily unstructured data, often stored in Web server access logs. While traditional domains for data mining, such as point of sale databases, have naturally defined transactions, there is no convenient method of clustering web references into transactions. This paper identifies a model of user browsing behavior that separates web page references into those made for navigation purposes and those for information content purposes. A transaction identification method based on the browsing model is defined and successfully tested against other methods, such as the maximal forward reference algorithm proposed in CPY96. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system MJH+96.
data mining, world wide web, association rules, web mining
R. Cooley, B. Mobasher, J. Srivastava, "Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns", KDEX, 1997, Knowledge and Data Exchange, IEEE Workshop on, Knowledge and Data Exchange, IEEE Workshop on 1997, pp. 2, doi:10.1109/KDEX.1997.629824