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Dispersion Measures and Multidistances on \(\mathbb {R}^k\)

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Book cover Soft Methods for Data Science (SMPS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 456))

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Abstract

After introducing a definition of dispersion measure on the Euclidean space \(\mathbb {R}^k\), we deal with the connection between these measures and the so called multidistances. In this way, we show that thr standard deviation is a relevant example of multidistance and, on the other hand, several significant families of multidistances are, at the same time, dispersion measures. Sufficient conditions for a multidistance to be a dispersion measure are also established.

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References

  1. Calvo T, Martín J, Mayor G (2012) Measures of disagreement and aggregation of preferences based on multidistances. In: Greco S et al (eds) IPMU 2012, Part IV, 549558. Springer, Berlin

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Acknowledgments

This work has been supported by the Spanish Government projects TIN2013-42795-P and TIN2014-56381-REDT (LODISCO).

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Correspondence to Javier Martín .

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Martín, J., Mayor, G. (2017). Dispersion Measures and Multidistances on \(\mathbb {R}^k\) . In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_43

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  • DOI: https://doi.org/10.1007/978-3-319-42972-4_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42971-7

  • Online ISBN: 978-3-319-42972-4

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