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Titlebook: Analysis of Symbolic Data; Exploratory Methods Hans-Hermann Bock,Edwin Diday Conference proceedings 2000 Springer-Verlag Berlin Heidelberg

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期刊全称Analysis of Symbolic Data
期刊简称Exploratory Methods
影响因子2023Hans-Hermann Bock,Edwin Diday
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学科分类Studies in Classification, Data Analysis, and Knowledge Organization
图书封面Titlebook: Analysis of Symbolic Data; Exploratory Methods  Hans-Hermann Bock,Edwin Diday Conference proceedings 2000 Springer-Verlag Berlin Heidelberg
影响因子Raymond Bisdorff CRP-GL, Luxembourg The development of the SODAS software based on symbolic data analysis was extensively described in the previous chapters of this book. It was accompanied by a series of benchmark activities involving some official statistical institutes throughout Europe. Partners in these benchmark activities were the National Statistical Institute (INE) of Portugal, the Instituto Vasco de Estadistica Euskal (EUSTAT) from Spain, the Office For National Statistics (ONS) from the United Kingdom, the Inspection Generale de la Securite Sociale (IGSS) from Luxembourg 1 and marginally the University of Athens . The principal goal of these benchmark activities was to demonstrate the usefulness of symbolic data analysis for practical statistical exploitation and analysis of official statistical data. This chapter aims to report briefly on these activities by presenting some signifi­ cant insights into practical results obtained by the benchmark partners in using the SODAS software package as described in chapter 14 below.
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Lars Holtkamp,Nils Arne Brockmannmplex type of data which we call . as they contain . and they are . In this context, we have a rapidly increasing need to extend standard data analysis methods (exploratory, graphical representations, clustering, factorial analysis, discrimination,…) to these symbolic data.
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https://doi.org/10.1007/978-3-322-80430-3se similarities as their data input. For example, in cluster analysis where we look for ‘homogeneous’ classes ., .,… of objects, it is typically required that pairs of objects from the . class have a . similarity (i.e., a . dissimilarity) and, conversely, that the similarity is . for pairs of objects from. classes (see Section 11.1).
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Symbolic Data Analysis and the SODAS Project: Purpose, History, Perspective,mplex type of data which we call . as they contain . and they are . In this context, we have a rapidly increasing need to extend standard data analysis methods (exploratory, graphical representations, clustering, factorial analysis, discrimination,…) to these symbolic data.
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Similarity and Dissimilarity,se similarities as their data input. For example, in cluster analysis where we look for ‘homogeneous’ classes ., .,… of objects, it is typically required that pairs of objects from the . class have a . similarity (i.e., a . dissimilarity) and, conversely, that the similarity is . for pairs of objects from. classes (see Section 11.1).
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