A Model for Extracting Most Desired Web Pages

A Model for Extracting Most Desired Web Pages

Jayanti Mehra (Maulana Azad National Institute of Technology, India) and Ramjeevan Singh Thakur (Maulana Azad National Institute of Technology, India)
DOI: 10.4018/978-1-7998-0186-3.ch007

Abstract

Weblog analysis takes raw data from access logs and performs study on this data for extracting statistical information. This info incorporates a variety of data for the website activity such as average no. of hits, total no. of user visits, failed and successful cached hits, average time of view, average path length over a website; analytical information such as page was not found errors and server errors; server information, which includes exit and entry pages, single access pages, and top visited pages; requester information like which type of search engines is used, keywords and top referring sites, and so on. In general, the website administrator uses this kind of knowledge to make the system act better, helping in the manipulation process of site, then also forgiving marketing decisions support. Most of the advanced web mining systems practice this kind of information to take out more difficult or complex interpretations using data mining procedures like association rules, clustering, and classification.
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Web Usage Mining (Wum): Overview

Data Sources

Information which is used for web study can be gathered at these three distinct locations (Jeba, Bhuvaneswari, & Muneeswaran, 2016).

Server Level: It is a sever computer that stores users’ behavior so the data are collected from different sources and for different users.

Client Level: It is client computer that store user’s browsing information like client browser, operating system etc.

Proxy server: It is an intermediate computer machine called proxy server. Where some browsing data are also resides therefore the weblog data should also be collected from the proxy server.

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Procedure Of Web Usage Mining

The procedure of web usage mining uses data mining techniques, to discover the interesting and fruitful patterns from web data that is understandable and which fulfills the requirements of clients searching on the web (Mobasher, Cooley, & Srivastava, 2000; Reddy, Reddy, & Sitaramulu, 2013; Singh & Badhe, 2014). Every data mining technique of web usage mining comprises these fundamental techniques i.e.

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