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Une nouvelle méthode de classification pour des données intervalles

A new clustering method for interval data
André Hardy and Nathanael Kasoro
p. 79-91

Abstracts

This paper presents a new clustering method for interval data. It is an extension of a classical clustering method to interval data. The classical procedure is based on the theory of point processes, and more particularly on the homogeneous Poisson process. The first part of the new method is a monothetic divisive procedure. The cut rule is an extension to interval data of the Hypervolumes clustering criterion. The pruning step uses two statistical likelihood ratio tests based on the homogeneous Poisson process: the Hypervolumes test and the Gap test. The output is a decision tree. The second part of the method is a merging process, that allows in particular cases to improve the classification obtained at the end of the first part of the algorithm. The method is applied to a generated data set and to a real data set. It is compared with other clustering methods available for interval data.

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References

Bibliographical reference

André Hardy and Nathanael Kasoro, “Une nouvelle méthode de classification pour des données intervalles”Mathématiques et sciences humaines, 187 | 2009, 79-91.

Electronic reference

André Hardy and Nathanael Kasoro, “Une nouvelle méthode de classification pour des données intervalles”Mathématiques et sciences humaines [Online], 187 | Automne 2009, Online since 15 December 2009, connection on 04 August 2021. URL: http://journals.openedition.org/msh/11138; DOI: https://doi.org/10.4000/msh.11138

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About the authors

André Hardy

Département de mathématique, FUNDP-Université de Namur, 8, Rempart de la Vierge, B-5000 Namur (Belgique), andre.hardy@fundp.ac.be

Nathanael Kasoro

Département de mathématique et informatique, Université de Kinshasa, B.P. 190, Kinshasa, République Démocratique du Congo, kasoro.mulenda@yahoo.fr

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Copyright

© École des hautes études en sciences sociales

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