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MIXISO: a Non-Hierarchical Clustering Method for Mixed-Mode Data

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Abstract

In this paper we consider the approach proposed in the hierarchical clustering method MIXCLAS to obtain a new non-hierarchical method which we named MIXISO. This method can analyze mixed mode data with a large number of units including in the presence of missing data. We demonstrate the application of this method on artificial and real data-set with missing values.

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References

  • Chiodi M. (1990) A partition type method for clustering mixed data, Italian Journal of Applied Statistics, vol. 2, n. 2.

    Google Scholar 

  • Diaconis P., Efron B. (1983). Computer-Intensive Methods in Statistics. Scientific American, Volume 248.

    Google Scholar 

  • Di Ciaccio A. (1992) Simultaneous Clustering of Qualitative and Quantitative data with Missing Observations, Italian Journal of Applied Statistics, v. 4, n. 4.

    Google Scholar 

  • Di Ciaccio A. (1999) MIXISO: a non-hierarchical clustering method for mixedmode data, CLADAG 1999, Book of short paper, Rome 5–6 July 1999.

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  • Everitt B.S., Merette, C. (1990) The clustering of mixed-mode data: a comparison of possible approaches, Journal ofApplied Statistics, v. 17, n. 3.

    Article  Google Scholar 

  • Gower J.C. (1971) A general coefficient of similarity and some of its properties, Biometrics, 27.

    Google Scholar 

  • Jardine N., Sibson R. (1971) Mathematical Taxonomy, Wiley London.

    Google Scholar 

  • Van Buuren S., Heiser W.J. (1989) Clustering N objects into K Groups under Optimal Scaling of variables, Psychometrika.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Economics, University of Urbino, Italy

    Agostino Di Ciaccio

Authors
  1. Agostino Di Ciaccio
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Editors and Affiliations

  1. Department SEFEMEQ, University of Rome “Tor Vergata”, Via di Tor Vergata, 00133, Rome, Italy

    Simone Borra

  2. Department SEGeS, University of Molise, Via de Sanctis, 86100, Campobasso, Italy

    Roberto Rocci

  3. Department Statistica, Probabilità e Statistiche Applicate, University of Rome “La Sapienza”, Piazza le Aldo Moro, 00185, Rome, Italy

    Maurizio Vichi

  4. University of Mannheim, 68131, Mannheim, Germany

    Martin Schader (Lehrstuhl für Wirtschaftsinformatik III Schloß) (Lehrstuhl für Wirtschaftsinformatik III Schloß)

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© 2001 Springer-Verlag Berlin Heidelberg

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Di Ciaccio, A. (2001). MIXISO: a Non-Hierarchical Clustering Method for Mixed-Mode Data. In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-59471-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41488-9

  • Online ISBN: 978-3-642-59471-7

  • eBook Packages: Springer Book Archive

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