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Representation of Concept Description by Multivalued Taxonomic Preordonance Variables

Israël-César Lerman 1 Philippe Peter 2
1 SYMBIOSE - Biological systems and models, bioinformatics and sequences
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 CID - Connaissances Informations Données
Abstract : Mathematical representation of complex data knowledge is one of the most important problems in Classification and Data Mining. In this contribution we present an original and very general formalization of various types of knowledge. The specific data are endowed with biological descriptions of phlebotomine sandfly species. Relative to a descriptive categorical variable, subsets of categories values have to be distinguished. On the other hand, hierarchical dependencies between the descriptive variables, associated with the mother ! daughter relation, have to be taken into account. Additionally, an ordinal similarity function on the modality set of each categorical variable. The knowledge description is formalized by means of a new type of descriptor that we call “Taxonomic preordonance variable with multiple choice”. Probabilistic similarity index between concepts described by such variables can be built.
keyword : Classification Complex data Structured data Probabilistic similarity
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https://hal.inria.fr/inria-00180101
Contributor : Israel-César Lerman Connect in order to contact the contributor
Submitted on : Wednesday, October 17, 2007 - 4:31:32 PM
Last modification on : Friday, February 4, 2022 - 3:24:10 AM

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  • HAL Id : inria-00180101, version 1

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INRIA | IRISA | CNRS | UNIV-RENNES1 | INSMI | IRISA-D7 | UR1-MATH-STIC | UR1-UFR-ISTIC | UNIV-RENNES | INSA-GROUPE | INSA-RENNES | UR1-MATH-NUM | EC-PARIS | INRIA2

Citation

Israël-César Lerman, Philippe Peter. Representation of Concept Description by Multivalued Taxonomic Preordonance Variables. Brito, P. and Bertrand, P. and Cucumel, G. and de Carvalho, F. Selected Contributions in Data Analysis and Classification, Springer, pp.271-284, 2007, Studies in Classification, Data Analysis, and Knowledge organization. ⟨inria-00180101⟩

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