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SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
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Decision Support Systems - Special issue on WITS '97
Clustering web documents: a phrase-based method for grouping search engine results
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A Concept-Driven Algorithm for Clustering Search Results
IEEE Intelligent Systems
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ACM SIGKDD Explorations Newsletter
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International Journal of Metadata, Semantics and Ontologies
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WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Collective User Behaviour and Tag Contextualisation in Folksonomies
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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ACM Computing Surveys (CSUR)
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ECDL'09 Proceedings of the 13th European conference on Research and advanced technology for digital libraries
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NLP4DL'09/AT4DL'09 Proceedings of the 2009 international conference on Advanced language technologies for digital libraries
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ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Improving quality of search results clustering with approximate matrix factorisations
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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This paper relates to a technique of improving results visualization in Web search engines known as search results clustering. We introduce an open extensible research system for examination and development of search results clustering algorithms - Carrot2. We also discuss attempts to measuring quality of discovered clusters and demonstrate results of our experiments with quality assessment when inflectionally rich language (Polish) is clustered using a representative algorithm - Suffix Tree Clustering.