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Clickstream Analysis of User Profile

Mihir B. Patel, Shyam Deshmukh


Clickstream data-the trail of digital footprints left by users as they click their way through a website-is loaded with valuable customer information for businesses. It is typically captured in semi-structured website log files. By discovering key strengths and limitations of these data used for research in marketing and user behaviour. The behaviour of users can be receive in log files store in web servers. By learning this user behaviour and making their profile and target them for different marketing techniques and promotions. Analysis of clickstream data and other user data gives a granular look at how individual customer segments are using the website. As a result bring in actionable insights to help personalize the user experience and convince more web visitors from browsers to buyers. In this research, we will analysis these logs and generate the user profiles. Website administrator use these user behaviour profile for internet marketing and online advertising promotions on website.

This paper includes the solution proposal on to improve conversion of user by various web analytics matrices with discriminant algorithm.


Clickstream Analysis, Hadoop, Map Reduce, Session Identification, User Identification

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“An Application For Clickstream Analysis”, C.E.Dinuca, International Journal Of Computers And Communications Issue 1, Volume 6, 2012

“Clickstream Log Acquisition With Web Farming”, Jia Hu And Ning Zhong,Dept. Of Information Engineering, Maebashi Institute Of Technology, Ieee/Wic/Acm International Conference On Web Intelligence (Wi’05)

“A New Method for Session Identification in Clickstream Analysis” Recent Researches in Tourism and Economic Development. Dumitru Ciobanu, Claudia Elena Dinuca

“Clickstream Analysis” Stefan Ziegler, December 2001

“Learning To Target: What Works For Behavioral Targeting” Sandeep Pandey, Mohamed Aly, Abraham Bagherjeiran, Andrew Hatch, Peter Ciccolo, Adwait Ratnaparkhi, Martin Zinkevich

“Case Study: E-Commerce Clickstream Visualization”,Jeffrey Brainerd Barry Becker, Ieee Symposium On Information Visualization 2001

“Anlysis of clickstream data using SAS” Sumit Sukhwani, Satish Garla and Goutam Chakraborty, Oklahoma State University,SAS global forum 2012

“Improving The Session Identification Using The Mean Time” C. E. Dinuca, D. Ciobanu,” International Journal Of Mathematical Methods In Applied Sciences, Volume 6, 2012.

R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules, IBM Almaden Research Center 1994

Priyanka Patil And Ujwala Patil” Preprocessing Of Web Server Log File For Web Mining”, Proceedings Of "National Conference On Emerging Trends In Computer Technology (Ncetct-2012)", April 21, 2012

“Characterizing Comparison Shopping Behavior:A Case Study”, Mona Gupta, Happy Mittal, Parag Singla, Amitabha Bagchi,Department Of Computer Science And Engineering, Indian Institute Of Technology New Delhi, India , Icde Workshops Ieee 2014

“Mining Web Browsing Patterns for E-Commerce.” Song, Q., Shepperd, M. Comput. Ind. 57(7), 622–630 (2006)

Web Usage Pattern Analysis Through Web Logs: A Review” Dilip Singh Sisodia, Shrish Verma, Ninth International Conference Of Computer Science And Software Engineering Vol 2 December 2012

“On Mining Web Access Logs” Joshi, A., Joshi, K., Krishnapuram, R, ACM SIGMOD Workshop DMKD, (2000)

“Case Study: E-Commerce Clickstream Visualization”,Jeffrey Brainerd Barry Becker, IEEE Symposium on Information Visualization 2001

Book-Web Analytics: An hour day, Avinash Kaushik

Book- Hadoop Architecture, Ted Malaska, Mark grover

Book- Data Mining Practical Machine Learning Tools and Techniques, Ian Witten, E Frank, Mark hall


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