Publication Date

Fall 2016

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

T. Y. Lin

Second Advisor

Robert Chun

Third Advisor

Monish Prabu Chandran


Data Mining World Wide Web Frequent Itemset


In this research, we predict User's Future Request using Data Mining Algorithm. Usage of the World Wide Web has resulted in a huge amount of data and handling of this data is getting hard day by day. All this data is stored as Web Logs and each web log is stored in a different format with different Field names like search string, URL with its corresponding timestamp, User ID’s that helps for session identification, Status code, etc. Whenever a user requests for a URL there is a delay in getting the page requested and sometimes the request is denied. Our goal is to generate a Frequent Pattern Itemset on the Web Log we have chosen and after analyzing and processing the data we apply Apriori All algorithm with a minimum support to prune and improve the Frequent Pattern and thereby predict the User's Future Request which will help the user in successfully reaching out to the URL pages he has requested.