Intelligent Information Retrieval Using Fuzzy Association Rule Classifier

Intelligent Information Retrieval Using Fuzzy Association Rule Classifier

Sankaradass Veeramalai (Anna University, India) and Arputharaj Kannan (Anna University, India)
DOI: 10.4018/978-1-4666-2047-6.ch010
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As the use of web applications increases, when users use search engines for finding some information by inputting keywords, the number of web pages that match the information increases at a tremendous rate. It is not easy for a user to retrieve the exact web page which contains information he or she requires. In this paper, an approach to web page retrieval system using the hybrid combination of context based and collaborative filtering method employing the concept of fuzzy association rule classification is introduced and the authors propose an innovative clustering of user profiles in order to reduce the filtering space and achieves sub-linear filtering time. This approach can produce recommended web page links for users based on the information that associates strongly with users’ queries quickly with better efficiency and therefore improve the recall, precision of a search engine.
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The primary goal of information systems is to retrieve or filter objects and classify them based on the rules described in the system. The filtering system may be classified as context based filtering or collaborative filtering (Zhou, Li, Bruza, Wu, Xuusing, & Lau, 2007; Wang, Xie, & Li, 2006). In information-filtering environments, uncertainties associated with changing interests of the user and the dynamic document stream is handled (Mostafa, Mukhopadhyay, Lam, & Palakal, 1997). The classification process is based on association rule mining technique where a rule is defined to make distinct classification. Recommendation using association rules is to predict preference for item k when the user preferred item i and j, by adding confidence of the association rules that have k in the result part and i or j in the condition part. Association rules capture relationships among items based on patterns of co-occurrence across transactions (Kumar & Thambidurai, 2004). The hierarchical structure is also used for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level (Dumais & Chen, 2000).

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