In this paper we propose a mixed analytical and graphical exploratory strategy based on data archetypes for the exploratory analysis of multivariate data. Our approach is of considerable help in exploring the periphery of the data scatter, exploiting an outward-inward perspective, to highlight small peripheral groups as well as anomalies, outliers and irregularities in the data cloud shape. The strategy is carried out in a comprehensive quantitative programming environment provided by the joint use of the software system R and of the visualization system GGobi. It provides a visualization system involving both static and dynamic graphics based on the so-called multiple views paradigm. The views are organized in a spreadplot and heavily exploit dynamics and interactive statistical graphics. © 2010 Springer-Verlag Berlin Heidelberg.
Exploring Data Through Archetypes / D'Esposito, M. R.; Ragozini, Giancarlo; Vistocco, D.. - STAMPA. - (2010), pp. 287-298. (Intervento presentato al convegno 11th Biennial Conference of the International Federation of Classification Societies, IFCS 2009 and with the 33rd Annual Conf of the German Classification Society (Gesellschaft fur Klassifikation) on Classification as a Tool fo Research, GfKl 2009 tenutosi a Dresden, deu nel 2009) [10.1007/978-3-642-10745-0-31].
Exploring Data Through Archetypes
RAGOZINI, GIANCARLO;Vistocco D.
2010
Abstract
In this paper we propose a mixed analytical and graphical exploratory strategy based on data archetypes for the exploratory analysis of multivariate data. Our approach is of considerable help in exploring the periphery of the data scatter, exploiting an outward-inward perspective, to highlight small peripheral groups as well as anomalies, outliers and irregularities in the data cloud shape. The strategy is carried out in a comprehensive quantitative programming environment provided by the joint use of the software system R and of the visualization system GGobi. It provides a visualization system involving both static and dynamic graphics based on the so-called multiple views paradigm. The views are organized in a spreadplot and heavily exploit dynamics and interactive statistical graphics. © 2010 Springer-Verlag Berlin Heidelberg.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
