Abstract
This paper describes an approach helping users to better understand the results of their queries. These results are structured with a clustering algorithm and described using a personal vocabulary. The goal is to find what the elements of a cluster have in common that also differentiates them from the elements of the other clusters. The data considered for characterizing each cluster of answers are not limited to attributes used in the query, revealing unexpected correlations to the user. The originality of this work resides in the definition and use of fuzzy-set-based characterizations and their properties.
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Acknowledgments
This work has been partially funded by the French DGE (Direction Générale des Entreprises) under the project ODIN (Open Data INtelligence).
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Moreau, A., Pivert, O., Smits, G. (2016). A Fuzzy Approach to the Characterization of Database Query Answers. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_27
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DOI: https://doi.org/10.1007/978-3-319-40581-0_27
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