%0 Journal Article
%T A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules
%A Binu Thomas
%A G. Raju
%J ISRN Artificial Intelligence
%D 2013
%R 10.1155/2013/316913
%X 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. 1. Introduction Classification is a Data Mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants in a bank as low, medium, or high credit risks. A classification task begins with a data set in which the class assignments are known. A classification model that predicts credit risk could be developed based on observed data for many loan applicants over a period of time. In addition to the historical credit rating, the data might track employment history, home ownership or rental, years of residence, number and type of investments, and so on. Credit rating would be the target, the other attributes would be the predictors, and the data for each customer would constitute a case. Classification techniques include decision trees, association rules, fuzzy systems, and neural networks. Classification has many applications in customer segmentation, business modeling, marketing, credit analysis, web mining and biomedical, and drug response modeling. Classification models include decision trees, Bayesian models, association rules, and neural nets. Although association rules have been predominantly used for data exploration and description, the interest in using them for prediction has rapidly increased in the Data Mining community. When
%U http://www.hindawi.com/journals/isrn.artificial.intelligence/2013/316913/