Distributed Learning Predictors through Web Log Mining using Bayesian Networks
Year: 2017
Type: article
Abstract: Distributed learning discusses the various strategies in which learners are separated but communicate between themselves through thelearning coaches. This makes the learners improve the learning skills and decrease learning times. A large part of hidden information resides ina weblog server (i.e.), user IP address, location, time, Number of entries. Web log designer can analyse these information and rank the weblogsbased on this information. In this paper weblog analysis of data is done through distributed system by using the Bayesian networks. Thebehaviour of a web site’s users may change so that the designer can trace out the user behaviour from web log to make predictions, according tothe frequent patterns accessed through the log files For the classification purpose Bayesian classifier is used which is based on the probabilitytheory.Keywords- weblogs; distributed node; distributed learner; Bayesian classifier; mining.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219851267