Discovering Ordinal Attributes Through Gradual Patterns, Morphological Filters and Rank Discrimination Measures

Abstract : This paper proposes to exploit heterogeneous data, i.e. data described by both numerical and categorical features, so as to gain knowledge about the categorical attributes from the numerical ones. More precisely, it aims at discovering whether, according to a given data set, based on information provided by the numerical attributes, some categorical attributes actually are ordinal ones and, additionally, at establishing ranking relations between the category values. To that aim, the paper proposes the 3-step methodology OSACA, standing for Order Seeking Algorithm for Categorical Attributes: it first consists in extracting gradual patterns from the numerical attributes, to identify rich ranking information about the data; it then applies mathematical morphology tools, more precisely alternated filters, to induce an associated order on the categorical attributes. The third step evaluates the quality of the candidate rankings through an original measure derived from the rank entropy discrimination.
Type de document :
Communication dans un congrès
SUM 2018 - 12th International Conference of Scalable Uncertainty Management, Oct 2018, Milan, Italy. Springer, SUM: Scalable Uncertainty Management, 11142, pp.152-163, 2018, Lecture Notes in Computer Science. 〈http://www.ir.disco.unimib.it/sum2018/〉. 〈10.1007/978-3-030-00461-3_11〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01893238
Contributeur : Anne Laurent <>
Soumis le : mercredi 24 octobre 2018 - 11:25:14
Dernière modification le : vendredi 16 novembre 2018 - 02:20:36

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Christophe Marsala, Anne Laurent, Marie-Jeanne Lesot, Maria Rifqi, Arnaud Castelltort. Discovering Ordinal Attributes Through Gradual Patterns, Morphological Filters and Rank Discrimination Measures. SUM 2018 - 12th International Conference of Scalable Uncertainty Management, Oct 2018, Milan, Italy. Springer, SUM: Scalable Uncertainty Management, 11142, pp.152-163, 2018, Lecture Notes in Computer Science. 〈http://www.ir.disco.unimib.it/sum2018/〉. 〈10.1007/978-3-030-00461-3_11〉. 〈lirmm-01893238〉

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