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The ultrametric properties of binary datasets

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Many multivariate algorithms commonly applied for binary datasets depend on a proper metric (i.e., dissimilarity function) imposed on binary vectors. In the following work the relationships between different metrics defined on the randomly generated binary datasets and the cophenetic correlation coefficient (CCC) will be presented.
Rocznik
Strony
69--83
Opis fizyczny
Bibliogr. 30 poz.
Twórcy
autor
  • Computer Laboratory, Poznań, Poland
Bibliografia
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  • 3. Choi S-S., Cha S-H, Tappert C.C.: A survey of binary similarity and distance measures. Journal of Systemics, Cybernetics and Informatics 8 (2010), 43–48.
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  • 10. Gower J.C., Legendre P.: Metric and Euclidean properties of dissimilarity coefficients. J. Classification 3 (1986), 5-48.
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  • 13. Ibrahim H.M., Marghny M.H., Abdelaziz N.M.A.: Fast vertical mining using Boolean algebra. Int. J. Adv. Comput. Sci. Appl. 6 (2015), 89–96.
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  • 20. Pathi S., Kothalanka A., Addala V.: Binary matrix approach for mining frequent sequential pattern in large databases. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4 (2014), 311–317.
  • 21. Podani J., Dickinson T.A.: Comparison of dendrograms: a multivariate approach. Can. J. Botany 62 (1984), 2765–2778.
  • 22. Sesli M., Yegenoglu E. D.: Comparison of similarity coefficients used for cluster analysis based on RAPD markers in wild olives. Genet. Mol. Res. 9 (2010), 2248–2253.
  • 23. Shirkhorshidi A.S., Aghabozorgi S., Wah T.Y.: A comparison study on similarity and dissimilarity measures in clustering continuous data. PloS ONE 10(12): e0144059 (2015), 1–20.
  • 24. Simovici D.A., Djeraba C.: Mathematical Tools for Data Mining. SpringerVerlag, London 2008.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-80abd79c-0696-42d6-b733-4ad829cd4206
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