Communications of the ACM
Incremental relevance feedback for information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
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
ACM Computing Surveys (CSUR)
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
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Information Retrieval on the World Wide Web
IEEE Internet Computing
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
ScentTrails: Integrating browsing and searching on the Web
ACM Transactions on Computer-Human Interaction (TOCHI)
Leaders-subleaders: an efficient hierarchical clustering algorithm for large data sets
Pattern Recognition Letters
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Information Processing and Management: an International Journal
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
Use of Fuzzy Rough Set Attribute Reduction in High Scent Web Page Recommendations
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Fast Single-Link Clustering Method Based on Tolerance Rough Set Model
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Elicitation and use of relevance feedback information
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Distance based fast hierarchical clustering method for large datasets
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
TI-DBSCAN: clustering with DBSCAN by means of the triangle inequality
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
A neighborhood-based clustering by means of the triangle inequality
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
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Information on the web is huge in size and to find the relevant information according to the information need of the user is a big challenge. Information scent of the clicked pages of the past query sessions has been used in the literature to generate web page recommendations for satisfying the information need of the current user. High scent information retrieval works on the bedrock of keyword vector of query sessions clustered using information scent. The dimensionality of the keyword vector is very high which affects the classification accuracy and computational efficiency associated with the processing of input queries and ultimately affects the precision of information retrieval. All the keywords in the keyword vector are not equally important for identifying the varied and differing information needs represented by clusters. Fuzzy Rough Set Attribute Reduction (FRSAR) has been applied in the presented work to reduce the high dimensionality of the keyword vector to obtain reduced relevant keywords resulting in improvement in space and time complexities. The effectiveness of fuzzy rough approach for high scent web page recommendations in information retrieval is verified with the experimental study conducted on the data extracted from the web history of Google search engine.