Clustering Effect on the Statistical Estimation Accuracy of Distribution Density

• Autores: Rimantas Rudzkis, Tomas Ruzgas
• Localización: Acta applicandae mathematicae, ISSN 0167-8019, Vol. 97, Nº. 1-3, 2007, págs. 211-219
• Idioma: inglés
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• Resumen

  • The paper is devoted to statistical nonparametric estimation of multivariate distribution density. The influence of data pre-clustering on the estimation accuracy of multimodal density is analyzed by means of the Monte Carlo method. It is shown that the soft clustering is more advantageous than the hard one. While a moderate increase in the number of clusters also increases the calculation time, it considerably reduces the estimation error.