Data Preparation for Mining World Wide Web Browsing Patterns

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Data Preparation for Mining World Wide Web Browsing Patterns (1999)

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by Robert Cooley , Bamshad Mobasher , Jaideep Srivastava

Venue:	KNOWLEDGE AND INFORMATION SYSTEMS
Citations:	566 - 43 self

BibTeX

@ARTICLE{Cooley99datapreparation,
    author = {Robert Cooley and Bamshad Mobasher and Jaideep Srivastava},
    title = {Data Preparation for Mining World Wide Web Browsing Patterns},
    journal = {KNOWLEDGE AND INFORMATION SYSTEMS},
    year = {1999},
    volume = {1},
    pages = {5--32}
}

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Abstract

The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of tra#c and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system [15].

Keyphrases

web site data preparation design task user session meaningful transaction web site design common traversal path straightforward statistic sheer volume sophisticated form server log data mining algorithm real world data usage analysis web server design webminer system page access frequency large web data repository several data preparation technique world wide web web usage mining important input association rule unique user

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