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

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/571855
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