Adding Knowledge Extracted by Association Rules into Similarity Queries

Abstract : In this paper, we propose new techniques to improve the quality of similarity queries over image databases performing association rule mining over textual descriptions and automatically extracted features of the image content. Based on the knowledge mined, each query posed is rewritten in order to better meet the user expectations. We propose an extension of SQL aimed at exploring mining processes over complex data, generating association rules that extract semantic information from the textual description superimposed to the extracted features, thereafter using them to rewrite the queries. As a result, the system obtains results closer to the user expectation than it could using only the traditional, plain similarity query execution.
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Article dans une revue
Journal of Information and Data Management, Brazilian Computer Society, 2010, 1 (3), pp.391-406. 〈http://seer.lcc.ufmg.br/index.php/jidm/article/view/64〉
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https://hal.archives-ouvertes.fr/hal-01093236
Contributeur : Richard Chbeir <>
Soumis le : mercredi 10 décembre 2014 - 13:56:58
Dernière modification le : mercredi 12 septembre 2018 - 01:28:01

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  • HAL Id : hal-01093236, version 1

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Monica Ribeiro Porto Ferreira, Marcela Xavier Ribeiro, Agma Traina, Richard Chbeir, Caetano Traina. Adding Knowledge Extracted by Association Rules into Similarity Queries. Journal of Information and Data Management, Brazilian Computer Society, 2010, 1 (3), pp.391-406. 〈http://seer.lcc.ufmg.br/index.php/jidm/article/view/64〉. 〈hal-01093236〉

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