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ISRN Artificial Intelligence
Volume 2013 (2013), Article ID 316913, 10 pages
http://dx.doi.org/10.1155/2013/316913
Research Article

A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules

1Department of BCA, Marian College, Kuttikkanam, Kerala, India
2Department of Information Technology, Kannur University, Kannur, Kerala, India

Received 30 September 2013; Accepted 20 October 2013

Academic Editors: J. García, J. A. Hernandez, and L. Mikhailov

Copyright © 2013 Binu Thomas and G. Raju. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.