Dealing with large amounts of data or data flows, it can be convenient or necessary to process them in different 'pieces'; if the data in question refer to different occasions or positions in time or space, a comparative analysis of data stratified in batches can be suitable. The present approach combines clustering and factorial techniques to study the association structure of binary attributes over homogeneous subsets of data; moreover, it seeks to update the result as new statistical units are processed in order to monitor and describe the evolutionary patterns of association. © Springer-Verlag Berlin Heidelberg 2013.
Dynamic data analysis of evolving association patterns / Iodice D'Enza, A.; Palumbo, Francesco. - (2013), pp. 45-53. (Intervento presentato al convegno Joint Meetings on Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2010 tenutosi a Firenze, ita nel 2010) [10.1007/978-3-642-28894-4-6].
Dynamic data analysis of evolving association patterns
Iodice D'Enza, A.;PALUMBO, FRANCESCO
2013
Abstract
Dealing with large amounts of data or data flows, it can be convenient or necessary to process them in different 'pieces'; if the data in question refer to different occasions or positions in time or space, a comparative analysis of data stratified in batches can be suitable. The present approach combines clustering and factorial techniques to study the association structure of binary attributes over homogeneous subsets of data; moreover, it seeks to update the result as new statistical units are processed in order to monitor and describe the evolutionary patterns of association. © Springer-Verlag Berlin Heidelberg 2013.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
