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OWA Operator‐Based Hybrid Framework for Outlier Reduction in Web Mining
Author(s) -
Gupta Ankit,
Kohli Shruti
Publication year - 2016
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21810
Subject(s) - outlier , data mining , computer science , operator (biology) , reduction (mathematics) , set (abstract data type) , web mining , web service , artificial intelligence , mathematics , world wide web , biochemistry , chemistry , geometry , repressor , transcription factor , gene , programming language
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.