This paper is published in Volume-4, Issue-3, 2018
Area
Computer Engineering
Author
Ujjwala Pandurang Patil, Akanksha Sahebrao Shejawal, Namrata Govind Ambekar, Nilu Dilip Shinde
Org/Univ
Shram Sadhana Bombay Trust’s College of Engineering and Technology, Jalgaon Maharashtra, India
Pub. Date
22 June, 2018
Paper ID
V4I3-1827
Publisher
Keywords
Weblog, User identification, Session identification, Profile generation

Citationsacebook

IEEE
Ujjwala Pandurang Patil, Akanksha Sahebrao Shejawal, Namrata Govind Ambekar, Nilu Dilip Shinde. Predicting user behaviour through the sessions of web mining, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Ujjwala Pandurang Patil, Akanksha Sahebrao Shejawal, Namrata Govind Ambekar, Nilu Dilip Shinde (2018). Predicting user behaviour through the sessions of web mining. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Ujjwala Pandurang Patil, Akanksha Sahebrao Shejawal, Namrata Govind Ambekar, Nilu Dilip Shinde. "Predicting user behaviour through the sessions of web mining." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

Nowadays users are concentrate on the complex task-oriented goal such as e-commerce site, making finances, making educational details. User divides the particular task into the number of small tasks and solves the multiple requests at the same time. The search engine keeps the track of queries depends on searched history. By extracting the user sessions from the log files and also sessions are extracted. Server-side log and client-side log are commonly used on the web. The server-side log is automatically generated according to each user request. Client-side logs will capture accurate, comprehensive usage data for usability analysis. The process includes the method of data cleaning, user identification, threshold selection, user profile generation, session identification. The mining is performed according to the frequency of user visiting each page. According to the generated sessions, the user behavior can be analyzed depending on time spent on each page.