The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence. © 2010 Springer-Verlag Berlin Heidelberg.
Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data / Palumbo, Francesco; Iodice D'Enza, A.. - STAMPA. - Studies in Classification, Data Analysis, and Knowledge Organization:(2010), pp. 45-55. (Intervento presentato al convegno 32nd Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, Data Handling and Business Intelligence, GfKl 2008 tenutosi a Hamburg, deu nel 2008) [10.1007/978-3-642-01044-6_4].
Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data
PALUMBO, FRANCESCO;A. Iodice D'Enza
2010
Abstract
The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence. © 2010 Springer-Verlag Berlin Heidelberg.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
