Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 42, Pages: 1-5
K . Dharmarajan1* and M. A. Dorairangaswamy2
*Author for correspondence
K . Dharmarajan
Department of Information Technology, Vels University, Chennai - 600117, Tamil Nadu, India; [email protected]
Objective: This article tries to discover the hidden knowledge and identifying user behavior on the web by using the web data sources. With the help of this knowledge, the overall performance of future accesses, the typical browsing behavior of a user and subsequently to predict desired pages, a user wants to access in future. Methods/Statistical Analysis: The user pattern is analyzed by using the modified Web Log Expert tool from the web access log file collected from the organization. This modified tool tries to conduct a web mining in a domain independent manner. This algorithm consists of three parts: 1. Given an input entity, extracting a set of IP addresses and visor lists and then ranking them according to comparability, 2. Extracting the domains in which the given entity takes part and 3. Identifying and summarizing the competitive evidence that details the organization’s strength. Findings: The main aim of the research work is extracting of user frequent access page using web log data, which is based on user session time, IP addresses, browser details, operating system, top user. This complete analysis work has been implemented in the Web Log Expert tool. The experimental results provide an easier way to navigate the website and improve the website design architecture. This work deliberates the detailed results of a website in a specific education domain application. We investigate the statistics of hourly based, daily based, week and monthly based report of the web usage patterns. The goal is to capture, model and analyze the behavioral patterns and profiles of users interacting with a website. The knowledge about users and their behavior on the web helps the organization benefits and leads directly to profit increase.
Keywords: Frequent Patten, Session Identification, User Behavior, Web Usage Mining
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