Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
The Knowledge Engineering Review
Tolerance rough set theory based data summarization for clustering large datasets
Transactions on rough sets XIV
Investigating the effectiveness of thesaurus generated using tolerance rough set model
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Enhancing search result clustering with semantic indexing
Proceedings of the Third Symposium on Information and Communication Technology
Lexicon-based Document Representation
Fundamenta Informaticae - Cognitive Informatics and Computational Intelligence: Theory and Applications
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Clustering is a powerful tool for knowledge discovery in text collections. The quality of document clustering depends not only on clustering algorithms but also on document representation models. We develop a hierarchical document clustering algorithm based on a tolerance rough set model (TRSM) for representing documents, which offers a way of considering semantics relatedness between documents. The results of validation and evaluation of this method suggest that this clustering algorithm can be well adapted to text mining.