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Archetypal Symbolic Objects

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Advances in Theoretical and Applied Statistics

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

Symbolic Data Analysis has represented an important innovation in statistics since its first presentation by E. Diday in the late 1980s. Most of the interest has been for the statistical analysis of Symbolic Data that represent complex data structure where variables can assume more than just a single value. Thus, Symbolic Data allow to describe classes of statistical units as a whole. Furthermore, other entities can be defined in the realm of Symbolic data. These entities are the Symbolic objects, defined in terms of the relationships between two different knowledge levels. This article aims at introducing a new type of SO based on the archetypal analysis.

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References

  1. Bock, H.H., Diday, E.: Analysis of symbolic data. Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (2000)

    Book  Google Scholar 

  2. Corsaro, S., Marino, M.: Archetypal analysis of interval data, Reliable Comput. 14, 105–116 (2010)

    MathSciNet  Google Scholar 

  3. Cutler, A., Breiman, L.: Archetypal analysis. Technometrics 36, 338–347 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  4. D’Esposito, M.R., Ragozini, G.: A New R-Ordering Procedure to Rank Multivariate Performances, Quaderni di Statistica 10, 5–21 (2008)

    Google Scholar 

  5. D’Esposito, M.R., Ragozini, G, Vistocco.: Exploring data through archetypes. In: Locarek-Junge, H., Weihs, C. (eds.) Classification as Tool for Reasearch, Series in Studies in Classification, Data Analysis, and Knowledge Organization, pp. 287–289. Springer, Berlin Heidelberg (2010)

    Google Scholar 

  6. D’Esposito, M.R., Palumbo, F., Ragozini, G.: Archetypal analysis for interval data in marketing research. Ital. J. Appl. Stat. 18, 343–358 (2006)

    Google Scholar 

  7. D’Esposito, M.R., Palumbo, F., Ragozini, G.: Interval archetypes: a new tool for interval data analysis. Stat. Anal. Data Min. 5(4), 322–335 (2012)

    Article  MathSciNet  Google Scholar 

  8. Diday, E.: Introduction à l’approche symbolique en analyse des données. RAIRO 23, 193–236 (1989)

    MathSciNet  MATH  Google Scholar 

  9. Diday, E., Noirhomme-Fraiture, M.: Symbolic Data Analysis and the SODAS Software. Wiley, Chichester (2008)

    MATH  Google Scholar 

  10. Elder, A., Pinnel, J.: Archetypal analysis: an alternative approach to finding defining segments. In: 2003 Sawtooth Software Conference Proceedings, pp. 113–129, Sequim, WA (2003)

    Google Scholar 

  11. Heavlin, W.D.: Archetypal analysis of computer performance. In: 38th Symposium on the INTERFACE on Massive Data Sets and Stream, Pasedena, CA (2007)

    Google Scholar 

  12. Kearfott, R.B.: Rigorous Global Search: Continuous Problems. Kluwer Academic Publishers, Dordrecht (1996)

    Book  MATH  Google Scholar 

  13. Li, S., Wang, P., Louviere, J., Carson, R.: Archetypal analysis: a new way to segment markets based on extreme individuals. In: A Celebration of Ehrenberg and Bass: Marketing Knowledge, Discoveries and Contribution, ANZMAC 2003 Conference Proceedings, pp. 1674–1679, Adelaide (2003)

    Google Scholar 

  14. Marinetti, S., Finesso, L., Marsilio, E.: Matrix factorisation methods: application to thermal NDT/E. NDT&E Int. 39, 611–616 (2006)

    Article  Google Scholar 

  15. Marinetti, S., Finesso, L., Marsilio, E.: Archetypes and principal components of an IR image sequence. Infrared Phys. Tech. 49, 272–276 (2007)

    Article  Google Scholar 

  16. Morris, L., Schmolze, R.: Consumer archetypes: a new approach to developing consumer understanding frameworks. J. Market. Res. 46, 289–300 (2006)

    Google Scholar 

  17. Porzio, G.C., Ragozini, G. Vistocco, D.: On the use of archetypes as benchmarks. Appl. Stoch. Model Bus. Ind. 24, 419–437 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Touati, M., Djedour, M., Diday, E.: Synthesis of objects. In: Gettler-Summa, M., Bottou, L., Goldfarb, B., Murtagh, F., Pardoux, C., Touati, M. (eds.) Statistical Learning and Data Science, Computer Science & Data Analysis series. CRC Press, Boca Raton (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Economiche e Statistiche, Università di Salerno, Via Ponte don Melillo, 84084, Fisciano, Italy

    M. R. D’Esposito

  2. Dipartimento di Scienze Politiche, Università degli Studi di Napoli Federico II, Via L. Rodinò 22, 80138, Napoli, Italy

    F. Palumbo & G. Ragozini

Authors
  1. M. R. D’Esposito
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  2. F. Palumbo
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  3. G. Ragozini
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Corresponding author

Correspondence to M. R. D’Esposito .

Editor information

Editors and Affiliations

  1. Department of Statistics, University of Trieste, P.le Europa 1, Trieste, 34127, Italy

    Nicola Torelli

  2. Department of Statistics, University of Padua, Via Cesare Battisti 241, Padova, 35121, Italy

    Fortunato Pesarin

  3. Université Paris Descartes, rue des Saints-Pères 45, Paris, 75270, France

    Avner Bar-Hen

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D’Esposito, M.R., Palumbo, F., Ragozini, G. (2013). Archetypal Symbolic Objects. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds) Advances in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35588-2_5

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