Communications of the ACM
Computational models of information scent-following in a very large browsable text collection
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Using information scent to model user information needs and actions and the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Modern Information Retrieval
Information Retrieval on the World Wide Web
IEEE Internet Computing
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
High scent web page recommendations using fuzzy rough set attribute reduction
Transactions on rough sets XIV
Tolerance rough set theory based data summarization for clustering large datasets
Transactions on rough sets XIV
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Information on the web is growing at a rapid pace and to satisfy the information need of the user on the web is a big challenge. Search engines are the major breakthrough in the field of Information Retrieval on the web. Research has been done in literature to use the Information Scent in Query session mining to generate the web page recommendations. Low computational efficiency and classification accuracy are the main problems that are faced due to high dimensionality of keyword vector of query sessions used for web page recommendation. This paper presents the use of Fuzzy Rough Set Attribute Reduction to reduce the high dimensionality of keyword vectors for the improvement in classification accuracy and computational efficiency associated with processing of input queries. Experimental results confirm the improvement in the precision of search results conducted on the data extracted from the Web History of "Google" search engine.