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Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model

Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model

Ratnesh Kumar Jain, Rahul Singhai
Copyright: © 2017 |Pages: 51
ISBN13: 9781522506133|ISBN10: 1522506136|EISBN13: 9781522506140
DOI: 10.4018/978-1-5225-0613-3.ch002
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MLA

Jain, Ratnesh Kumar, and Rahul Singhai. "Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model." Web Usage Mining Techniques and Applications Across Industries, edited by A.V. Senthil Kumar, IGI Global, 2017, pp. 29-79. https://doi.org/10.4018/978-1-5225-0613-3.ch002

APA

Jain, R. K. & Singhai, R. (2017). Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model. In A. Kumar (Ed.), Web Usage Mining Techniques and Applications Across Industries (pp. 29-79). IGI Global. https://doi.org/10.4018/978-1-5225-0613-3.ch002

Chicago

Jain, Ratnesh Kumar, and Rahul Singhai. "Analysis of User's Browsing Behavior and Their Categorization Using Markov Chain Model." In Web Usage Mining Techniques and Applications Across Industries, edited by A.V. Senthil Kumar, 29-79. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0613-3.ch002

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Abstract

Web server log file contains information about every access to the web pages hosted on a server like when they were requested, the Internet Protocol (IP) address of the request, the error code, the number of bytes sent to the user, and the type of browser used. Web servers can also capture referrer logs, which show the page from which a visitor makes the next request. As the visit to web site is increasing exponentially the web logs are becoming huge data repository which can be mined to extract useful information for decision making. In this chapter, we proposed a Markov chain based method to categorize the users into faithful, Partially Impatient and Completely Impatient user. And further, their browsing behavior is analyzed. We also derived some theorems to study the browsing behavior of each user type and then some numerical illustrations are added to show how their behavior differs as per categorization. At the end we extended this work by approximating the theorems.

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