Marking and Generalization by Symbolic Objects in the Symbolic Official Data Analysis Software

Summa, Mireille Gettler

doi:10.1007/978-3-642-59789-3_65

Mireille Gettler Summa⁸

Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization ((STUDIES CLASS))

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Abstract

In this paper we propose an automatic method of generating Symbolic Objects in the following framework: description of a partition by symbolic objects that takes into account two aspects, that may be called homogeneity and discrimination criteria. This method belongs to a family of algorithms named MGS (Marking and Generalization by Symbolic Objects), which may be applied either to Factorial Analysis interpretation, to interpretation of partitions or for summarizing huge databases.

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Authors and Affiliations

Lise Ceremade, Université Paris IX Dauphine, 1, Place du Ml. de Lattre de Tassigny, 75016, Paris, France
Mireille Gettler Summa

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Mireille Gettler Summa
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Editors and Affiliations

University of Groningen Heymans Institute (PA), Grote Kruisstraat 2/1, NL-9712 TS, Groningen, The Netherlands
Henk A. L. Kiers
Facultés Universitaires Notre-Dame de la Paix, University of Namur, Rempart de la Vierge, 8, B-5000, Namur, Belgium
Jean-Paul Rasson (Directeur du Department de Mathématique) (Directeur du Department de Mathématique)
Data Theory Group Department of Education, Leiden University, P.O. Box 9555, NL-2300 RB, Leiden, The Netherlands
Patrick J. F. Groenen
Lehrstuhl für Wirtschaftsinformatik III Schloß, University of Mannheim, D-68131, Mannheim, Germany
Martin Schader

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Summa, M.G. (2000). Marking and Generalization by Symbolic Objects in the Symbolic Official Data Analysis Software. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_65

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DOI: https://doi.org/10.1007/978-3-642-59789-3_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67521-1
Online ISBN: 978-3-642-59789-3
eBook Packages: Springer Book Archive

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