Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers

The search and identification of outliers is a fundamental step, generally preparatory to the elaborations aimed at obtaining consistent results. The new approach devised for the identification of outliers in space R2 benefits from geometric / statistical techniques largely independent from the typ...

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Autor principal:	Maurizio Rosina
Formato:	article
Lenguaje:	EN IT
Publicado:	mediaGEO soc. coop. 2018
Materias:	Cartography GA101-1776 Cadastral mapping GA109.5
Acceso en línea:	https://doaj.org/article/08698e4e118f4e2baea7e541e8450d77
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spelling	oai:doaj.org-article:08698e4e118f4e2baea7e541e8450d772021-11-09T17:50:50ZBig Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers10.48258/geo.v22i1.15201128-81322283-5687https://doaj.org/article/08698e4e118f4e2baea7e541e8450d772018-05-01T00:00:00Zhttps://www.mediageo.it/ojs/index.php/GEOmedia/article/view/1520https://doaj.org/toc/1128-8132https://doaj.org/toc/2283-5687 The search and identification of outliers is a fundamental step, generally preparatory to the elaborations aimed at obtaining consistent results. The new approach devised for the identification of outliers in space R2 benefits from geometric / statistical techniques largely independent from the type of data distribution, and is based on four methodological pillars: clustering, the convex hull peeling technique, a specific metric and Chebyshev's inequality, which is valid for any type of univariate distribution of values. The modularity and the generality of the approach, coupled to the research and identification of outliers based on strictly statistical parameters, make the approach presented a useful and daily tool for those who need to process bivariate data with the security of being able to previously identify outliers. Maurizio RosinamediaGEO soc. coop.articleCartographyGA101-1776Cadastral mappingGA109.5ENITGEOmedia, Vol 22, Iss 1 (2018)
institution	DOAJ
collection	DOAJ
language	EN IT
topic	Cartography GA101-1776 Cadastral mapping GA109.5
spellingShingle	Cartography GA101-1776 Cadastral mapping GA109.5 Maurizio Rosina Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers
description	The search and identification of outliers is a fundamental step, generally preparatory to the elaborations aimed at obtaining consistent results. The new approach devised for the identification of outliers in space R2 benefits from geometric / statistical techniques largely independent from the type of data distribution, and is based on four methodological pillars: clustering, the convex hull peeling technique, a specific metric and Chebyshev's inequality, which is valid for any type of univariate distribution of values. The modularity and the generality of the approach, coupled to the research and identification of outliers based on strictly statistical parameters, make the approach presented a useful and daily tool for those who need to process bivariate data with the security of being able to previously identify outliers.
format	article
author	Maurizio Rosina
author_facet	Maurizio Rosina
author_sort	Maurizio Rosina
title	Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers
title_short	Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers
title_full	Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers
title_fullStr	Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers
title_full_unstemmed	Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers
title_sort	big data...a few outliers = big mistakes. un nuovo processo per l'individuazione di outliers
publisher	mediaGEO soc. coop.
publishDate	2018
url	https://doaj.org/article/08698e4e118f4e2baea7e541e8450d77
work_keys_str_mv	AT mauriziorosina bigdataafewoutliersbigmistakesunnuovoprocessoperlindividuazionedioutliers
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Big Data...a few Outliers = Big Mistakes. Un nuovo processo per l'individuazione di outliers

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