Dynamic Data Analysis of Evolving Association Patterns

  • Alfonso Iodice D’Enza
  • Francesco Palumbo
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


Statistical Unit Multiple Correspondence Analysis Association Structure Binary Attribute Homogeneous Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Università di CassinoCassinoItaly
  2. 2.Università degli Studi di Napoli Federico IINaplesItaly

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