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- SourceAvailable from: Jothi Venkateswaran[Show abstract] [Hide abstract]
ABSTRACT: Categorization of Web Users and Web Pages are the fundamental tasks of Web Personalization. In this paper it is proposed a Matrix Based Fuzzy Clustering Approach MBFCA and experimentally evaluated the approach for the effective discovery of web user clusters and web page clusters. The use of MBFCA enables the generation of clusters that can capture the Web user's navigation behavior based on their interest. In web usage analysis, many a times there are no sharp boundary between clusters. Hence fuzzy clustering is better suited for Web Usage Mining. Experimental results presented, the clusters generated by applying MBFCA, are intended to be used to make recommendations by suggesting interesting links to the user.
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ABSTRACT: Web usage mining aims to discover interesting user access patterns from web logs. Web usage mining has become very critical for effective web site management, creating adaptive web sites, business and support services, personalization and so on. In this paper, an efficient approach for frequent pattern mining using web logs for web usage mining is proposed and this approach is called as HFPA. In this approach HFPA, the proposed technique is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. This technique is applied and its performance is compared with that of classical Apriori-mined rules. The results indicate that the proposed approach HFPA not only generates far fewer rules than Apriori-based algorithms (FPA), the generated rules are also of comparable quality with respect to three objective performance measures, Confidence, Lift and Conviction. Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper effective pruning techniques are proposed that are characterized by the natural web link structures. Experiments showed that interestingness measures can successfully be used to sort the discovered association rules after the pruning method was applied. Most of the rules that ranked highly according to the interestingness measures proved to be truly valuable to a web site administrator.
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ABSTRACT: Chen, Cybenko, Funahashi, Babri, Gorban, Barron and others studied function approximation capabilities by feed forward neural networks. Ramakrishnan and his collaborators introduced the left sigmoidal and right sigmoidal signals to prove some function approximation theorems in continuous functions. In this paper, the right sigmoidal signal is used to establish function approximation theorems using infimum and supremum operators.
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