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Analyzing Symbolic Data

Problems, Methods, and Perspectives

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Cooperation in Classification and Data Analysis

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Abstract

Classical data analysis considers data vectors with real-valued or categorical components. In contrast, Symbolic Data Analysis (SDA) deals with data vectors whose components are intervals, sets of categories, or even frequency distributions. SDA generalizes common methods of multivariate statistics to the case of symbolic data tables. This paper presents a brief survey on basic problems and methods of this fast-developing branch of data analysis. As an alternative to the current more or less heuristic approaches, we propose a new probabilistic approach in this context. Our presentation concentrates on visualization, dissimilarities, and partition-type clustering for symbolic data.

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Authors and Affiliations

  1. Institute of Statistics, RWTH Aachen University, 52056, Aachen, Germany

    H.-H. Bock

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  1. H.-H. Bock
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Correspondence to H.-H. Bock .

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    Bock, HH. (2009). Analyzing Symbolic Data. In: Gaul, W., Bock, HH., Imaizumi, T., Okada, A. (eds) Cooperation in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00668-5_1

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