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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01893238
Contributor : Anne Laurent <>
Submitted on : Wednesday, October 24, 2018 - 11:25:14 AM
Last modification on : Wednesday, March 27, 2019 - 1:34:11 AM
Long-term archiving on : Friday, January 25, 2019 - 2:33:28 PM

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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. pp.152-163, ⟨10.1007/978-3-030-00461-3_11⟩. ⟨lirmm-01893238⟩

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