Why Fuzzy Sequential Patterns can Help Data Summarization: an Application to the INPI Trademark Database

Abstract : Mining fuzzy rules is one of the best ways to summarize large databases while keeping information as clear and understandable as possible for the end-user. Several approaches have been proposed to mine such fuzzy rules, in particular to mine fuzzy association rules. However, we argue that it is important to mine rules that convey information about the order. For instance, it is very interesting to convey the idea of time running in rules, which is done in fuzzy sequential patterns. In this paper, we thus focus on fuzzy sequential patterns. We show that mining such rules requires to manage a lot of information and we propose algorithms to remain efficient in both memory use and computation time. Our proposition is assessed by experiments. Particularly, we apply our algorithms on the INPI database which stores almost 2 million trademarks
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Communication dans un congrès
IEEE. IEEE World Congress on Computational Intelligence, Jul 2006, Vancouver, BC (Canada), pp.3596-3603, 2006
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00095901
Contributeur : Martine Peridier <>
Soumis le : lundi 18 septembre 2006 - 15:12:18
Dernière modification le : jeudi 24 mai 2018 - 15:59:23
Document(s) archivé(s) le : mardi 6 avril 2010 - 01:01:14

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Céline Fiot, Anne Laurent, Maguelonne Teisseire, Bénédicte Laurent. Why Fuzzy Sequential Patterns can Help Data Summarization: an Application to the INPI Trademark Database. IEEE. IEEE World Congress on Computational Intelligence, Jul 2006, Vancouver, BC (Canada), pp.3596-3603, 2006. 〈lirmm-00095901〉

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