OWA Operator-Based Hybrid Framework for Outlier Reduction in Web Mining.

Web mining is the process of extracting useful information from Web resources. Handling outliers is one of the primary challenges of present Web mining techniques. The complex nature of the Web, by virtue of both data and users, makes it very difficult to mine the information and convert to knowledge base with little outlier values. In this paper, a framework for reducing outliers in regression analysis with the help of ordered weighted operators OWA as a multicriteria decision-making problem is being formulated. First, a regression problem with a real-time Web data set will be formulated followed by solving the same with the help of a variant of OWA operators. Results, thus obtained are able to prove that outliers can be reduced significantly with the help of proposed approach.