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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Arabie, P., & Hubert, L. (1994). Cluster analysis in marketing research. IEEE Transactions on Automatic Control,19, 716–723.
Borg, I., & Groenen, P. (2005). Modern multidimensional scaling. New York: Springer.
Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (1999). Using association rules for product assortment decisions: A case study. In Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, California, United States (pp. 254–260). New York: ACM.
Greenacre, M. J. (2007) Correspondence analysis in practice (2nd ed.). Boca Raton: Chapman and Hall/CRC.
Hwang, H., Dillon, W. R., & Takane, Y. (2006). An extension of multiple correspondence analysis for identifying heterogenous subgroups of respondents. Psychometrika,71, 161–171.
Iodice D’Enza, A., & Greenacre, M.J. (2010). Multiple correspondence analysis for the quantification and visualization of large categorical data sets. In Proceedings of SIS09 Statistical Methods for the Analysis of Large Data-Sets, Pescara. Padova: CLEUP.
Mirkin B. (2001). Eleven ways to look at the Chi-squared coefficient for contingency tables. The American Statistician,55(2), 111–120.
Palumbo F., & Iodice D’Enza A. (2010). A two-step iterative procedure for clustering of binary sequences. In: Data analysis And classification (pp. 50–60). Berlin: Springer.
Vichi M., & Kiers H. (2001). Factorial k-means analysis for two way data. Computational Statistics and Data Analysis,37(1), 49–64.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
D’Enza, A.I., Palumbo, F. (2013). Dynamic Data Analysis of Evolving Association Patterns. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-28894-4_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28893-7
Online ISBN: 978-3-642-28894-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)