A Survey on Analysis of User Behavior on Digital Market by Mining Clickstream Data

  • Praveen Kumar Padigela
  • R. SugunaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1090)


Data stream mining has emerged as one of the most prominent areas with its applications in various areas like network sensors, stock exchange, meteorological research and e-commerce. Stream mining is potentially an active area in which the data is continuously generated in large amounts which are dynamic, non-stationary, unstoppable, and infinite in nature. One of such streaming data generated with the user browsing tendency is Clickstream data. Analyzing the user online behavior on e-commerce Web sites is helpful in drawing certain conclusions and making specific recommendations for both the users and the electronic commerce companies to improve their marking strategies and increase the transaction rates effectively leading to enhance the revenue. This paper aims at presenting a survey of different methodologies and parameters used in analyzing the behavior of a user through Clickstream data. Little deeper, this article also outlines the methods used so far for clustering the users based on mining their interests.


Clickstream Behavior Digital market Collaborative filtering 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennaiIndia

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